- Comp-neuro - lists.cnsorg.org

Open PhD Positions in Dendritic Processing for Novel Computing Concepts

by Robert Legenstein

The Lab of Robert Legenstein at TU Graz is offering funded PhD positions in the area Deep Learning and Computational Neuroscience. A background in Deep Learning, Computataional Neuroscience, Computer Science, or Mathematics is desirable, as well as evidence for excellent prior performance as a student. Successful candidates will investigate models of computation and learning in neuronal networks with nonlinear dendritic processing and develop learning algorithms for optical processing hardware. She/he will be embedded in the interdisciplinary consortium of the EU-FET-open project ADOPD (Adaptive Optical Dendrites). See https://www.tugraz.at/institute/igi/people/prof-legenstein/projects/#c204996 for a brief project description. Please send your application including a CV, any prior thesis and/or work, and trascript of records by 15. September 2020 to robert.legenstein(a)igi.tugraz.at Dr. Robert Legenstein Institute Head Institute of Theoretical Computer Science Graz University of Technology Inffeldgasse 16b/I, 8010 Graz, Austria https://www.tugraz.at/institute/igi/people/prof-legenstein/ Follow @CompLegenstein on Twitter. ++43/316/873-5824 ---------------------------------- -- Dr. Robert Legenstein Associate Professor Institute Head Institute for Theoretical Computer Science Graz University of Technology Inffeldgasse 16b/I, 8010 Graz, Austria http://www.igi.tugraz.at/legi/ Follow @CompLegenstein on Twitter. ++43/316/873-5824 ----------------------------------

Sept. 1, 2020

Reminder: Special Entropy Issue "Information Theory in Computational Neuroscience"

by Dimitrije Markovic

Dear Colleagues, We cordially invite you to submit a manuscript for possible publication in a Special Issue on "Information Theory in Computational Neuroscience" to be published in the journal Entropy (ISSN 1099-4300, IF 2.530, http://www.mdpi.com/journal/entropy) The aim of the Special Issue is to bring together neuronal models and neuronal plasticity mechanisms that are grounded in information theory principles, modern inference, and learning algorithms. We welcome submissions that use information theory as the basis for defining generative principles of neuronal dynamics over multiple spatio-temporal scales, which informs our understanding of information processing in the brain. You can see full details of the scope and submission details at: https://www.mdpi.com/journal/entropy/special_issues/Computational_Neuroscie… The submission deadline is 30 September 2020. You may send your manuscript at any time before the deadline or a bit later. Submitted papers should not be under consideration for publication elsewhere. Entropy is fully open access. Open access (unlimited and free access by readers) increases publicity and promotes more frequent citations, as indicated by several studies. Please kindly note that an Article Processing Charge (APC) of CHF 1600 currently applies to all accepted papers. Please do not hesitate to come back to us if there is anything further that we can help with. We look forward to hearing from you. Kind regards, Dimitrije Markovic and Claudius Gros ============================= Dr. Dimitrije Markovic Post-Doc at Technische Universität Dresden, Department of Psychology, Dresden, Germany Lab website: https://tu-dresden.de/mn/psychologie/ifap/ni Phone: +49-351-463-43146 Personal website: http://dimarkov.github.io/ =============================

Sept. 1, 2020

NeuTouch Summer School

by Miguel Casal Santiago

The Horizon 2020 project Neutouch is glad to announce that the first summer school of the project about “Touch for prosthetics” will be held online the 21 & 28 September and 5 & 12 October 2020. The school will have talks from cutting-edge professors and students in the field of touch, including topics such as neuromorphic engineering, brain-computer interfaces, computational neuroscience and machine learning, among others. We invite you to take a look at the program together with a small description of the talks in this link <https://www.neutouch.eu/events/neuschool20>, where you can also subscribe for free. You can contact neutouchboard(a)gmail.com <mailto:neutouchboard@gmail.com> for doubts and requests Neutouch Editorial Board

Aug. 31, 2020

Postdoc positions in Aarhus University, Denmark

by Diego Vidaurre

Dear all, Funded by an ERC grant, I am opening two postdoc positions in my lab in Aarhus University in collaboration with Oxford University, to develop new methods on machine learning and Bayesian time series modelling, with application to specific problems in computational neuroscience. If someone is interested, or know of someone interested, please get in contact. More details on the topics, salary, conditions, requirements, etc. here: https://health.au.dk/en/about-health/vacant-positions/job/postdoc-in-integr… Thanks Diego Vidaurre

Aug. 30, 2020

Postdoc in computational cognitive neuroscience of speech processing at the University of Texas at Austin (USA)

by Hamilton, Liberty

The Hamilton Lab at The University of Texas at Austin seeks to hire a full-time postdoctoral fellow to study brain processing of natural speech in the presence of noise. Our approaches include computational methods (neural networks, linear/nonlinear regression models, encoding/decoding), electrophysiology (scalp EEG and intracranial EEG), and fMRI. A postdoc is sought for a one-year contract to begin a collaborative project with the Huth Lab on brain processing of natural sounds using scalp EEG and fMRI. Lab websites: https://slhs.utexas.edu/research/hamilton-lab and https://cs.utexas.edu/~huth/ Start date: Open Full time, benefits eligible, one-year contract with potential for extension based on performance/funding. Qualifications: The ideal candidate will have a strong quantitative background (time series analysis, regression models) and ability to code in python or MATLAB (though python will be used for most projects). All candidates must hold a PhD by the time of appointment. Preferred Qualifications: Previous experience in scalp EEG and/or fMRI strongly preferred, though related work in other electrophysiology methods will also be considered. A PhD in neuroscience, computer science, cognitive science, psychology, bioengineering, communication disorders, or other related fields is suggested. The Hamilton Lab is located in the Department of Speech, Language, and Hearing Sciences and is jointly affiliated with the Department of Neurology at Dell Medical School. The Huth Lab is located in the Departments of Neuroscience and Computer Science. The Hamilton and Huth labs are committed to fostering a supportive and diverse environment. We are strongly committed to mentoring our students and postdoctoral scholars to reach their goals. Our groups work as a multidisciplinary team, where each member’s contribution is valued. Our lab members come from a wide variety of fields including speech/language/hearing science, neuroscience, linguistics, music, physics, computer science, and psychology. The candidate is expected to work collaboratively with graduate students and research assistants in these fields. Apply at: https://utaustin.wd1.myworkdayjobs.com/UTstaff/job/UT-MAIN-CAMPUS/Postdocto…

Aug. 28, 2020

Professor position in Goettingen, Germany

by Rizzoli, Silvio

Dear colleagues, I hope this email finds you well. My institute at the University Medical Center Goettingen, Germany, the Institute for Neuro- and Sensory Physiology, currently has an announcement for a W2 professorship position in the field of computational modeling of synaptic physiology. Our institute produces substantial quantitative data on synapses, from fluorescence imaging to biochemistry, mass spectrometry and electron microscopy (see for example Wilhelm et al., Science, 2014). We are looking for an investigator with substantial expertise in computational aspects, who would benefit from these data, and would be able to use them for important studies. This professorship will be integrated in an excellent neuroscience campus, and will benefit strongly from our collaborative research center CRC/SFB 1286, dedicated to Quantitative Synaptology (composed of more than 20 collaborating laboratories, from many excellent institutions). Our announcement can be found at: <https://jobs.zeit.de/jobs/universitaetsprofessur-w2-modellierung-synaptisch…>https://www.academics.com/jobs/university-professorship-w2-modeling-synapti… It would be great if you could also send this announcement to colleagues in your department, who may be interested in applying for this position. Best wishes, and thank you in advance, Silvio Rizzoli Prof. Dr. Silvio O. Rizzoli UNIVERSITÄTSMEDIZIN GÖTTINGEN GEORG-AUGUST-UNIVERSITÄT Direktor des Instituts für Neuro- und Sinnesphysiologie Zentrum Physiologie und Pathophysiologie Humboldtallee 23, 37073 Göttingen Telefon: 0551-39-5911 FAX: 0551-39–66031 E-Mail: srizzol(a)gwdg.de Sprecher des Centers Biostructural Imaging of Neurodegeneration (BIN) von-Siebold-Straße 3a, 37075 Göttingen Sprecher des Sonderforschungsbereichs 1286 Quantitative Synaptologie (Collaborative Research Center 1286 Quantitative Synaptology)

Aug. 26, 2020

Professorship in computational modeling of synaptic physiology at the University Medical Center Göttingen, Germany

by Erik De Schutter

The University Medical Center Göttingen, Germany has announced a W2 professorship position in the field of computational modeling of synaptic physiology. The institute produces substantial quantitative data on synapses, from fluorescence imaging to biochemistry, mass spectrometry and electron microscopy. They are looking for an investigator with substantial expertise in computational aspects, who would benefit from these data, and would be able to use them for important studies. This professorship will be integrated in an excellent neuroscience campus, and will benefit strongly from our collaborative research center CRC/SFB 1286, dedicated to Quantitative Synaptology (composed of more than 20 collaborating laboratories, from many excellent institutions). The announcement can be found at: https://www.academics.com/jobs/university-professorship-w2-modeling-synapti…

Aug. 26, 2020

SMILES virtual workshop @ICDL 2020

by Xavier Hinaut

The SMILES (Sensorimotor Interaction, Language and Embodiment of Symbols) Workshop will take place virtually at the ICDL 2020 (International Conference on Developmental Learning). * Call for abstracts and papers: - Deadline: 25th of September 2020 - papers abstracts: 2 pages - long papers: 4-6 pages - Submissions: smiles.conf(a)gmail.com <mailto:smiles.conf@gmail.com> - Papers format: same as ICDL conference https://cis.ieee.org/publications/t-cognitive-and-developmental-systems/tcd… <https://cis.ieee.org/publications/t-cognitive-and-developmental-systems/tcd…> - Workshop dates (coming soon): the days before or after the ICDL conference (26-30 October 2020) * Workshop Short Description On the one hand, models of sensorimotor interaction are embodied in the environment and in the interaction with other agents. On the other hand, recent Deep Learning development of Natural Language Processing (NLP) models allow to capture increasing language complexity (e.g. compositional representations, word embedding, long term dependencies). However, those NLP models are disembodied in the sense that they are learned from static datasets of text or speech. How can we bridge the gap from low-level sensorimotor interaction to high-level compositional symbolic communication? The SMILES workshop will address this issue through -an interdisciplinary approach involving researchers from (but not limited to): - Sensori-motor learning, - Emergent communication in multi-agent systems, - Chunking of perceptuo-motor gestures (gestures in a general sense: motor, vocal, ...), - Sensori-motor learning, - Symbol grounding and symbol emergence, - Compositional representations for communication and action sequence, - Hierarchical representations of temporal information, - Language processing and acquisition in brains and machines, - Models of animal communication, - Understanding composition and temporal processing in neural network models, and - Enaction, active perception, perception-action loop. * More info - contact: smiles.conf(a)gmail.com <mailto:smiles.conf@gmail.com> - organizers: Xavier Hinaut, Clement Moulin-Frier, Silvia Pagliarini, Joni Zhong, Loo CHU KIONG, Michael Spranger, Tadahiro Taniguchi - invited speakers (coming soon) - workshop website (comming soon): https://sites.google.com/view/smiles-workshop/ <https://sites.google.com/view/smiles-workshop/> - ICDL conference website: https://cdstc.gitlab.io/icdl-2020/ <https://cdstc.gitlab.io/icdl-2020/> * Workshop Long Description Recently Deep Learning networks have broken many benchmarks in Natural Language Processing (NLP), e.g. [1]. Such breakthroughs are realized by a few mechanisms (e.g. continuous representations like word embedding, attention mechanism, ...). However, no equivalent finding happened towards the understanding of which mechanisms enable the brain to perform similar functions. Deep Learning does not reproduce learning mechanisms nor the dynamics of the brain. The brain needs to parse incoming stimuli and learn from them incrementally, it cannot unfold time like deep learning algorithms such as Back-propagation through time (BPTT). Thus, we still lack the key neuronal mechanisms needed to properly model the (hierarchies of) functions in language perception and production. Other models of language processing reproducing the behavior of brain dynamics (Event-Related-Potentials (ERPs) [2] or functional Magnetic Resonance Imaging (fMRI) [3]) have been developed. However, such model softens lack explanatory power demonstrating the causes of such observed dynamics: i.e. what is computed and why is it computed – for which purpose? We need more biologically plausible learning mechanisms while producing causal explanations of the experimental data modelled. There is converging evidence that language production and comprehension are not separated processes in a modular mind, they are rather interwoven, and this interweaving is what enables people to predict themselves and each other [4].Interweaving of action and perception is important because it allows a learning agent (or a baby) to learn from its own actions: for instance, by learning the perceptual consequences(e.g. the heard sounds) of its own actions (e.g. vocal productions) during babbling [5]. Thus, the agent learns in a self-supervised way instead of relying only on supervised learning, which in contrast, imply non-biological teacher signals cleverly designed by the modeler. Explicit neuronal models explaining which are the mechanisms shaping these perceptuo-motor units through development are missing. The existence of sensorimotor (i.e. mirror) neurons at abstract representation levels (often called action-perception circuits [6]), jointly with the perceptuo-motor shaping of sensorimotor gestures [5], suggest the existence of similar action-perception mechanisms implemented at different levels of hierarchy. How could we go towards such hierarchical architectures based on action-perception mechanisms? Importantly, a language processing model needs a way to acquire the semantics of the (symbolic) perceptuo-motor gestures and of the more abstract representations, otherwise it would consider only morphosyntactic and prosodic featuresof language. These symbolic gestures, i.e. signs, need to be grounded to the mental concept, i.e. signified, they are representing. Several theories and robotic experiments give examples of how symbols could be grounded or how symbols could emerge [7]. However, current neurocomputational models aiming to explain brain processes are not grounded. Robotics have an important role here for the grounding of semantics by experiencing the world through interactions with the physical world and with humans. We need mechanisms that start from raw sensory perception and raw motor commands in order to let emerge plausible representations through development, instead of arbitrary representations. Finally, computational models of emergent communication in agent populations are currently gaining interest in the machine learning community [8], [9]. These contributions show how a communication system can emerge to solve cooperative tasks in sequential environments. However, they are still relatively disconnected from the earlier theoretical and computational literature aiming at understanding how language might have emerged from a prelinguistic substance [10], [11]. We need to conceive communication as the emergent result of a collective behavior optimization process and to ground the resulting computational models into the theoretical literature from language evolution research. The SMILES workshop will aim at discussing each of the questions mentioned above, together with original approaches on how to integrate them. [1] J. Devlin et al., 2018. [2] H. Brouwer and J. C. J. Hoeks, 2013. [3] M. Garagnani et al. 2008. [4] M. J. Pickering and S. Garrod, 2013. [5] J.-L. Schwartz et al., 2012. [6] F. Pulvermüller and L. Fadiga, 2010. [7] T. Taniguchi et al., 2016. [8] S. Sukhbaatar et al.,2016. [9] I. Mordatch and P. Abbeel, 2017. [10] M. Tomasello et al., 2005. [11] P.-Y. Oudeyer and L. Smith, 2015. Xavier Hinaut Inria Researcher (CR) Mnemosyne team, Inria LaBRI, Université de Bordeaux Institut des Maladies Neurodégénératives +33 5 33 51 48 01 www.xavierhinaut.com

Aug. 21, 2020

Postdoc/PhD/RA positions at ÖAW Acoustics Research Institute, Vienna

by Robert Baumgartner

We are currently looking for enthusiastic researchers at all levels to join an exciting interdisciplinary research consortium addressing „Dynamic Auditory Predictions in Human and Non-human Primates (Dynamates)". It rests on a strong collaboration between cognitive neuroscience, cognitive biology, and computational modeling – for more information, visit our Dynamates website <https://www.kfs.oeaw.ac.at/dynamates>. The following positions are immediately available under my supervision at the Acoustics Research Institute of the Austrian Academy of Sciences: Postdoc <https://www.oeaw.ac.at/fileadmin/subsites/Jobs/ISF083PD220.pdf> focussing on computational models explaining the causal relationship between neural activity (EEG) and perception/behaviour in humans PhD Student <https://www.oeaw.ac.at/fileadmin/subsites/Jobs/ISF082DOC220.pdf> focussing on computational models for comparative behaviour Research Assistant <https://www.oeaw.ac.at/fileadmin/subsites/Jobs/ISF081STUD220.pdf> helping with (mainly EEG) data collection They are open until appropriate candidates are found. The 1st round of evaluation will start on Sept 1, 2020. Feel free to distribute! Best, Robert — Robert Baumgartner, PhD YIRG Leader | Research Associate Acoustics Research Institute Austrian Academy of Sciences Wohllebengasse 12-14, 1040 Vienna, Austria +43 1 51581-2527 Robert.Baumgartner(a)oeaw.ac.at https://www.kfs.oeaw.ac.at/baumgartner Like us on Facebook <https://www.facebook.com/SCHALLFORSCHUNG.OEAW/> Follow us on Twitter/oeaw <https://twitter.com/oeaw?lang=en> Visit us on Youtube <https://www.youtube.com/channel/UCY3rUdfN-VCjvfUWojWkayQ>

Aug. 19, 2020

Phd/Postdoc position for research on interpretable Deep Learning and brain modelling

by Wolfgang Maass

We are looking for a highly talented and motivated Phd student or Postdoc to complement our team in the Faculty for Computer Science and Biomedical Engineering of Graz University of Technology in Austria. The researcher will work on advancing Deep Learning and methods for understanding its outcomes ("interpretable AI") for applications to large data-based cortical microcircuit models, using state-of-the-art supercomputing facilities. The goals are to elucidate the organization of computing and learning in neural networks of the brain, and to develop on this basis spike-based AI methods. The research will be based on recently developed algorithms and TensorFlow software for spiking neural networks, as published for example in G. Bellec, D. Salaj, A. Subramoney, R. Legenstein, and W. Maass. Long short-term memory and learning-to-learn in networks of spiking neurons. 32nd Conference on Neural Information Processing Systems (NIPS 2018), https://igi-web.tugraz.at/PDF/243.pdf G. Bellec, F. Scherr, A. Subramoney, E. Hajek, D. Salaj, R. Legenstein, and W. Maass. A solution to the learning dilemma for recurrent networks of spiking neurons. Nature Communications, 2020. .https://www.nature.com/articles/s41467-020-17236-y.pdf F. Scherr, C. Stoeckl, and W. Maass. One-shot learning with spiking neural networks. bioRxiv, 2020. https://igi-web.tugraz.at/PDF/256.pdf This is a fully funded research position in the Human Brain Project of the EU, which offers an exciting international interdisciplinary research environment. Requirements: --strong mathematical background and analytical skills --deep interest in understanding the function of neural networks in the brain --excellent programming skills --very good knowledge in Machine Learning Please send your CV, grades, motivation letter, and links to pdf of your master thesis and other relevant preceding work to charlotte.rumpf(a)tugraz.at -- Prof. Dr. Wolfgang Maass Institut fuer Grundlagen der Informationsverarbeitung Technische Universitaet Graz Inffeldgasse 16b , A-8010 Graz, Austria Tel.: ++43/316/873-5822 Fax ++43/316/873-5805 http://www.igi.tugraz.at/maass/Welcome.html

Aug. 18, 2020

Neurodata Without Borders (NWB) User Days Workshop - September 21-23

by Ryan Ly

Neurodata Without Borders (NWB) is a data standard for neurophysiology, providing neuroscientists with a common standard to share, archive, use, and build analysis tools for neurophysiology data. NWB is designed to store a variety of neurophysiology data, including data from intracellular and extracellular electrophysiology experiments, data from optical physiology experiments, behavioral data, and stimulus data. The NWB Team will be hosting the 9th NWB User Days Workshop remotely on September 21-23. The workshop schedule will be centered on Central European Time (UTC+2) and will focus primarily on user training, including lessons for complete beginners to NWB. Come learn how to get your data into the NWB data standard and use state-of-the-art data analysis, visualization, and management tools with NWB, e.g., Brainstorm, CaImAn, calciumImagingAnalysis, DANDI, DataJoint, MIES, NWB Explorer, NWB Widgets, SpikeInterface, and suite2p. Event page: https://neurodatawithoutborders.github.io/nwb_hackathons/HCK09_2020_Remote/ Register here: https://bit.ly/2PHbE34 (it's free) Learn more about NWB at our website: https://www.nwb.org/ Best, The NWB Team -- Ryan Ly Scientific Data Engineer Lawrence Berkeley National Laboratory 1 Cyclotron Road Mail Stop 59R4104 Berkeley, CA 94720 Email: rly(a)lbl.gov Pronouns: he/him

Aug. 17, 2020

Neural Data Analysis Online Course

by Maryam

You are invited to “Neural Data Analysis Online Course” Dear Sir, Madam; ScienceBeam, which is a forefront technology provider of neuroscience applications, would like to take the opportunity to invite you for 10-session “Neural Data Analysis Online Course” that is going to start on September 7, 2020. During this course you will learn: ( Programming fundamentals: Variables, plots, loops ( How to compare ERPs in a statistical way: confidence intervals and bootstrapping ( EEG spectrum and spectrogram: from time to frequency and time-frequency ( More on spectrum: windowing and zero padding ( Analysis of coupled rhythms: covariance and coherence ( Filtering, filtering, and filtering! ( Cross-frequency coupling and Hilbert transform ( Brain states in sleep and Hidden Markov Model Hamed Yeganegi, PhD researcher in systems neuroscience, Technical University of Munich, Germany, is the instructor of this course. He is also a member of German Neuroscience Society (NWG) and Member of Federation of European Neuroscience Societies (FENS). Registration is open until September 4th. You won’t want to miss this! Don’t hesitate and register from the link below: Register NOW ! If you needed more information please contact us via Whatsapp or Mail: Whatsapp number: +86 133 8078 1282 Email: workshop(a)sciencebeam.com www.sciencebeam.com

Aug. 17, 2020

One Fellow position for closed-loop systems in neuroengineering @IIT Genova, ITALY

by Michela Chiappalone

Dear Comp-Neuro community, A new opening for a 12-months fellowship position at the Pre-Doctoral level is available in our team, under the supervision of Dr Stefano Buccelli and myself at the Rehab Technologies Lab of the Istituto Italiano di Tecnologia (IIT), located in Genova at the IIT Center for Convergent Technologies. The fellow will carry out research on the development of a novel closed-loop architecture for applications in neuroengineering. More information on the position can be found below in this email. The application link can be found here: https://iit.taleo.net/careersection/ex/jobdetail.ftl?lang=en&job=2000002C The call expires on September 7th, 2020. Please forward this message to all the interested students. Thank you and best regards, Michela Chiappalone & Stefano Buccelli ---------------------------------------------------------------------------------------------------------------------------------------------------------------- One Fellow position for the development of a reference architecture for closed-loop systems for neuroengineering applications The Rehab Technologies INAIL-IIT lab, (http://rehab.iit.it/) at Istituto Italiano di Tecnologia (IIT), Genova, Italy, is seeking to appoint one Fellow at the pre-Doctoral level to carry out research on the development of novel closed-loop architecture for applications in neuroengineering. The Rehab Technologies Lab is an innovation lab aimed at developing new high tech prosthetic, orthotic and rehabilitation devices of high social impact and market potential. The Lab is primarily focused on disability and rehabilitation, and exploits a co-creation process which involves scientists, patients and therapists in all the phases of the design, realization and testing of devices developed within the lab. The Neuroengineering area of Rehab Technologies is focused on neural interfaces and neurorehabilitation. The team has a multidisciplinary expertise in engineering and neuroscience, with skills in in vitro/in vivo electrophysiology, signal processing of neural data (spikes, LFP, EEG), closed-loop systems, neurostimulation and innovative neuroprostheses. The primary research goals of the team consist of designing and testing novel 'personalized' neuroengineering solutions for brain repair and extracting functional biomarkers of recovery induced by the rehab-related training. In modern neuroscience, closed-loop systems delivering stimulation depending on to the actual brain state demonstrated to improve the efficacy of a therapy, also reducing the number of side effects. To build a closed-loop system, three steps are needed: reading, interpreting and writing the neural code. To 'read' the neural code, we need to record neural activity, by using either invasive or non-invasive techniques. 'Interpreting' or decoding the neural activity is obtained by means of (real-time) signal processing techniques. To 'write' the neural code, usually an electrical simulation approach is adopted (but other options are available). Nowadays, given the increased number of recording sites that can be acquired at the same time, there is a big demand for high-performance signal processing techniques in both clinical practice and basic research. Field-Programmable Gate Arrays (FPGA) are one of the most interesting options for the implementation of high-performance computations and are usually chosen as a first step towards the development of standalone devices. Learning how to implement an algorithm on FPGA can be prohibitive for non-experts and thus it is preventing many neuroscientists from realizing innovative closed-loop systems. Within this framework and to reduce the entry barrier in the development of closed-loop architectures on FPGA, this project will built up a rapid-prototyping, reference architecture for closed-loop neuroengineering applications by exploiting a model-based design approach (in Simulink). This will be reached by means of the following specific objectives: 1. Build a reference Simulink model to process neural data. 2. Take advantage of the HDL coder and the Fixed-Point Designer to implement the model on FPGA; 3. Establish use cases on commercially available systems (e.g. INTAN Stimulation/Recording system). The project will be carried out at the Rehab Technologies Lab of IIT, located in Genova at IIT Center for Convergent Technologies. To face this technological challenge, the Rehab Technologies Lab will share resources, competencies and motivation to achieve excellent results transferable to the neuroengineering field and, later, to clinics. We are looking for a highly motivated, outstanding candidate with the following profile: - A solid background in biomedical/electronics engineering - Strong programming skills, especially with the MathWorks products (Matlab, Simulink) - Master Degree in one of the above field (already obtained or to be obtained in the next two months) - Good communication skills and ability to cooperate - Proficiency in English Desirable skills: - Experience with Verilog/VHDL During this period, the selected candidate will develop the following skills and experience: - Principle of Neuroengineering and its applications - Model-based Design and Rapid Prototyping - Closed-loop architectures - Basic Electrophysiological skills - Interactions within a multidisciplinary Lab and for an International project To apply, please send your CV, title of the Master Thesis and (foreseen) date of graduation, cover letter and name and contacts of one/two referees using the form online. Deadline: September 7, 2020. Istituto Italiano di Tecnologia (IIT), with its headquarters in Genova, Italy, is a non-profit institution with the primary goal of creating and disseminating scientific knowledge and strengthening Italy's technological competitiveness. The institute offers state-of-the-art equipment and a top-level interdisciplinary research environment focused on robotics and computer vision, neuroscience, drug discovery, nanoscience and technology. Istituto Italiano di Tecnologia is an Equal Opportunity Employer that actively seeks diversity in the workforce. Please note that the data that you provide will be used exclusively for the purpose of professional profiles' evaluation and selection, and in order to meet the requirements of Istituto Italiano di Tecnologia. Your data will be processed by Istituto Italiano di Tecnologia, based in Genoa, Via Morego 30, acting as Data Controller, in compliance with the rules on protection of personal data, including those related to data security. Please also note that, pursuant to articles 15 et. seq. of European Regulation no. 679/2016 (General Data Protection Regulation), you may exercise your rights at any time by contacting the Data Protection Officer (phone +39 010 71781 - email: dpo[at]iit.it). ------------------------------------------------------------------------- Dr Michela Chiappalone, PhD Researcher Rehab Technologies IIT-INAIL Lab Phone : +39 010 71781743 E-mail : michela.chiappalone(a)iit.it<mailto:michela.chiappalone@iit.it> ISTITUTO ITALIANO DI TECNOLOGIA Via Morego 30, 16163 Genova Site: www.iit.it<http://www.iit.it>

Aug. 17, 2020

Web Conference /DMSE 2020, Denmark - Deadline Approaching[Third Batch Call for Submissions] .

by Fredrick Johnson

International Conference on Data Mining and Software Engineering (DMSE 2020) September 26 ~ 27, 2020, Copenhagen, Denmark *WIKICFP Call* : http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=111118&copyownerid… *DBWORLD Call* : http://www.cs.wisc.edu/dbworld/messages/2020-08/1597292447.html Scope & Topics International Conference on Data Mining and Software Engineering (DMSE 2020) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Data mining and Software Engineering. The goal of this conference is to bring together researchers and practitioners from academia and industry to focus on understanding Data mining and modern software engineering concepts and establishing new collaborations in these areas. Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of data mining and software engineering. Topics of interest include, but are not limited to, the following • Advanced Topics in Software Engineering • Data Mining Applications • Computer Education • Data Mining in Modeling, Visualization, Personalization and Recommendation • Data Mining Systems and Platforms, Efficiency, Scalability and Privacy • Embedded System and Software • Foundations, Algorithms, Models, and Theory • Knowledge Processing • Knowledge-based Systems and Formal Methods • Languages and Formal Methods • Managing Software Projects • Mining Text, Semi-Structured, Spatio-Temporal, Streaming, Graph, Web, Multimedia • Multimedia and Visual Software Engineering • Quality Management • Search Engines and Information Retrieval • Software Engineering Decision Making • Software Engineering Practice • Software Maintenance and Testing • Software Process • Web Engineering • Web-based Education Systems and Learning Applications Accepted Papers List Learning for E-Learning-Aalen University, Germany. Magnetic Resonance Image Classification of Major Depression Disorder Based on Deep Learning-Beijing Technology and Business University, Beijing, China COSM: Controlled Over-sampling Method. A Methodological Proposal to Overcome the Class Imbalance Problem in Data Mining-CIRA (Italian Aerospace Research Centre),Italy A Process for Complete Autonomous Software Display Validation And Testing (Using A Car-cluster)-SAP Labs India Pvt Lmt.,India Analysis of the Displacement of Terrestrial Mobile Robots in Corridors Using Paraconsistent Annotated Evidential Logic Et-Bialystok University of Technology,Poland A Study on the Minimum Requirements for the On-line, Efficient and Robust Validation of Neutron Detector Operation and Monitoring of Neutron Noise Signals using Harmony Theory Networks-University of Piraeus, France Penalized Bootstrapping for Reinforcement Learning in Robot Control-University of Bonn,Germany Deep Reinforcement Learning for Navigation in Cluttered Environments-University of Bonn, Germany New Hybrid Artificial Intelligent Models Basedon Optimized-support Vector Machine and Locallylinear Neuro fuzzy for the Supplier Assessment Problem-Islamic Azad University, Iran IoT Learning Model for Smart Universities: Architecture, Challenges, and Applications-Whitecliffe College of Technology & Innovation, New Zealand The Principles of the Law General on the Protection of Personal Data and their Importance-Paulista University, Brazil. Controlled Machine Text Generation of Football Articles-University of Warsaw, Poland On the Comparison of Deep Neural Networks for Document Retrieval-Institute for Community Medicine, Germany Evaluationn of Company Investment value based on Machine Learning-Beijing University of Technology, China Performance evaluation of Precoded Band Codes and Hamming Norm Decoders in Random Linear Network Coding-National Engineering School of Tunis, Tunisia Neurological Signals Compression and Encryption for Security Transmission Based on IOMT: A Tele-neurological Diagnosis-University of Anbar, Iraq. Paper Submission Authors are invited to submit papers through the conference Submission System . Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this conference. The proceedings of the conference will be published by Computer Science Conference Proceedings in Computer Science & Information Technology (CS & IT) series (Confirmed). Here’s where you can reach us : dmse(a)dmse2020.org or dmsesecretary(a)gmail.com Submit your work Today! Regards, Fredrick.

Aug. 13, 2020

Post-doctoral Research position at Johns Hopkins BME

by Adam Charles

Dear Comp-neuro community: Please see below for a post-doctoral opportunity in my lab at The Johns Hopkins University Department of Biomedical Engineering. Link: https://www.bme.jhu.edu/ascharles/open-positions/ Warmest regards, -Adam ----------------------- Adam S. Charles Assistant Professor Department of Biomedical Engineering The Johns Hopkins University Baltimore, MD, 21218 ----------------------------------------------------------------------------------- I am pleased to announce a postdoctoral position in computational imaging, computational neuroscience and data science at the Johns Hopkins University Department of Biomedical Engineering in the lab of Adam Charles. This project is aimed at advancing the capabilities of functional fluorescence microscopy for neural recordings via new computational tools, including machine learning “at the sensor” and machine learning/signal processing for image analysis and interpretation. Neural imaging is an exciting field and this position offers opportunities to develop a range of new models and algorithms to address the unique challenges in fast and accurate fluorescence imaging and analysis. In addition, this project offers opportunities to interface with experimental collaborators. Applicants should hold a PhD in Engineering, Mathematics, Physics, Neuroscience or a related field with a strong research record. Applications should have demonstrated the ability to work collaboratively on quantitative problems. Applicants with experience in machine learning, signal processing, compressive sensing, computational imaging or computational neuroscience are especially encouraged to apply. Excellent written and oral communication skills in English are also required. This position is open immediately and will remain open until filled. The position is initially for 12 months with the possibility and expectation of renewal. Compensation will be commensurate with relevant experience. Candidates should send a CV, a statement of research experience and interests, expected date of availability, and the contact information for three references to adamsc(a)jhu.edu with the subject line "Computational imaging postdoc". Applications will be reviewed on a rolling basis and should be received by September 1 for full consideration. If the current COVID19 situation will delay the ability to apply by this deadline, please send a short note indicating that you intend to apply. Johns Hopkins University is a leader in biomedical engineering, imaging science, and neuroscience. The Department of Biomedical Engineering at Hopkins, along with the Center for Imaging Science (CIS), Kavli Institute for Neuroscience and Mathematical Institute for Data Science (MINDS) form a strong interdisciplinary computational community. JHU has competitive benefits (including comprehensive medical insurance) and is an equal opportunity employer.

Aug. 11, 2020

PhD position on human resource rationality

by Dominik Endres

We offer a PhD position in the research training group RTG 2271 "Breaking Expectations" https://www.uni-marburg.de/en/fb04/rtg-2271[1] in the department of Psychology at the Philipps-Universität Marburg. In this sub-project, we investigate whether individual humans are Bayesian resource- rational learners under naturalistic conditions where possible (virtual reality), and in online experiments. Specifically, we will compare the learning dynamics of these models and their dealing with expectation violations to human behavior. One possible application direction are models of chronic and acute pain perception and prediction. The project requires a keen interest in the computational and mathematical modeling of human experimental data. Interest in designing and carrying out such experiments is a plus. Benefits of membership in the GRK 2271 include: - scientific exchange with a vibrant community of PhD students and PIs of the other projects - possibility of (funded) lab visits with international mentors/collaborators - yearly retreats in the scenic Kleinwalsertal Formal requirements are: * a qualified degree in psychology, or computer science, or cognitive science, or engineering, or physics (Master, Diploma, or comparable) as well as * demonstrable programming experience in a high-level language, e.g. Python or Matlab. We expect you to be interested in: * the topic of the project (to be documented in a motivation letter [1 page max], in which you may refer to prior experiences, for instance, a thematically pertinent thesis, student research assistant positions, etc.) * quantitative modeling * developing virtual-reality scenarios and technology for research purposes Advantageous qualifications are: * prior exposure to machine learning, associative, Bayesian or other mathematical learning models and Bayesian statistics * willing to learn basic German language skills Application deadline: 28.08.20 For further information, please contact Prof. Dominik Endres (dominik.endres@uni- marburg.de[2]). The official job advert can be found here (deadline has been extended to 28.08.20): https://www.uni-marburg.de/de/universitaet/administration/verwaltung/dezern… personalabteilung/bewerber/stellen/wissenschaftliche-stellen/fb04-0026-wmz-310720- engl.pdf[3] -- Prof. Dr. Dominik Endres AE Theoretische Kognitionswissenschaft Allgemeine und Biologische Psychologie FB Psychologie, Gutenbergstr 18, 35032 Marburg, Germany Tel. +49-(0)6421-28-23818 -------- [1] https://www.uni-marburg.de/en/fb04/rtg-2271/beschreibung [2] mailto:dominik.endres@uni-marburg.de [3] https://www.uni-marburg.de/de/universitaet/administration/verwaltung/dezern… personalabteilung/bewerber/stellen/wissenschaftliche-stellen/fb04-0026-wmz-310720- engl.pdf

Aug. 11, 2020

Senior Scientist – Stroke position at the NeuroRestore center (Lausanne, Switzerland)

by Milekovic Tomislav

Senior Scientist – Stroke position at the NeuroRestore center (Lausanne, Switzerland) Location The Defitech Center for interventional Neurotherapies (NeuroRestore) is a research and innovation center joining EPFL’s lab of Prof. Gregoire Courtine and the University Hospital of Lausanne (CHUV) lab of Prof. Jocelyne Bloch. We conceive, develop and apply medical therapies aimed to restore neurological functions. To this end, we integrate implantable neurotechnologies with innovative treatments developed through rigorous preclinical and clinical studies. By working with our network of vibrant high-tech start-ups and established medical technology companies, we are committed to validate our medical therapy concepts. Our overarching goal is to see our medical therapies used every day in hospitals and rehabilitation clinics worldwide. Opportunity Over the last decade, research labs under the NeuroRestore umbrella have achieved breakthroughs for the treatment of spinal cord injury and Parkinson’s disease. Our recent studies indicate that similar approaches may lead to novel therapies for stroke. Therefore, we decided to build a strong research core focused on gaining deeper understanding of stroke and resulting neurological deficits, and on developing therapies that leverage the gained understanding to replace or restore motor functions impacted by stroke. By integrating well-equipped and expertly staffed rodent, non-human primate and clinical research facilities, NeuroRestore provides an ideal substrate for rapidly developing, integrating and clinically validating cutting-edge concepts of medical therapies, with the capacity to push successfully proven concepts into the technology transition phase. Position Summary The Senor Scientist – Stroke will bring their expertise and experience to build and then lead the stroke research core of the NeuroRestore center. They will develop original research projects across the whole stroke therapy development spectrum, starting from mechanistic understanding of stroke and resulting neurological and motor deficits, over preclinical tests of therapy concepts designed to take advantage of uncovered mechanisms, to clinical validation of therapies designed to alleviate motor deficits secondary to stroke. They will form and lead teams of NeuroRestore postdoctoral scientists, doctoral students, engineers and clinicians to execute these projects. They will train other scientists in their expert techniques and supervise junior scientists working on their projects. They will disseminate the results of their projects through research publications and conference talks and support preparation of scientific conference presentations and publications of other NeuroRestore personnel that works on stroke-related projects. They will take part in writing grant application that will support stroke-related projects. Responsibilities - Identify unmet clinical needs of stroke survivors and monitor the development of the medical neurotech sector to find opportunities for novel, feasible and rapidly realizable stroke therapies - Conceive, organize and plan out research projects - Form and leads teams to execute conceived research projects - Utilize their academic / clinical industry network to form collaborations that will help execute conceived research projects - Assess the project needs, identify relevant personnel and resources, and take part in hiring technical and scientific personnel to build skilled project teams - Train the NeuroRestore personnel in their area of expertise - Run project progress meetings, report on project progress, plan and manage the dissemination of project results - Take part in writing of funding applications to initiate new projects, strengthen / accelerate ongoing projects, or enhance ongoing collaborations - Develop and maintain connections with science labs and clinical centers to facilitate rapid animal and clinical testing of concepts and prototypes of medical treatments - Manage efforts to obtain regulatory approvals for animal studies and clinical trials to validate the concepts of medical therapies - Represent the Neurorestore Center at symposiums and conferences through talks and presentations Personal Traits - Passionate about the development of neurotechnologies that aim to alleviate movement disabilities of stroke - Thriving in a cutting edge, fast-paced, multidisciplinary environment - Self-starter and independent worker with an ability to identify innovative approaches and solutions - Attention to detail - Independent thinker and passionate problem solver Skills - Ability to independently conceive original research projects - Capacity to supervise several research projects in parallel - Capability to successfully lead research teams, and maintain collaborations with external partners - Strong verbal and written presentation skills in English - Experience with leading animal research projects and/or projects under the framework of clinical trials and studies - Track record of obtaining regulatory approvals for animal studies and/or clinical trials - Strong experience with writing project proposals, project progress reports and research articles - Experience in disseminating project progress through talks, presentation and publications - Experience in physiological signal acquisition and signal analysis - Capacity to identify skill gaps and hire personnel to fill them - Experience with writing, filling and obtaining patents - Advanced presentation skills (PowerPoint, Photoshop, Illustrator) - Willingness to travel in order to form / maintain collaborations - Possessing a strong academic, clinical, regulatory and industry network and the ability to use it to organize collaborative projects Experience - Doctoral degree (PhD) - 6 or more years of experience working in the fields of neuroscience / neuroengineering (including the PhD studies) - 3 or more years of experience in stroke-related research - Strong publication record in the stroke field Contact Applications including a CV and a cover letter describing your background and interest should be sent to tomislav.milekovic(a)epfl.ch<mailto:tomislav.milekovic@epfl.ch>. Informal inquiries are welcome.

Aug. 9, 2020

PhD position in machine learning at EPFL (Lausanne, Switzerland)

by Milekovic Tomislav

PhD position in the lab of Prof. Gregoire Courtine at EPFL (Lausanne, Switzerland) Machine learning techniques to develop and enhance computational models of the spinal cord Location: The laboratory of Prof. Gregoire Courtine at the Swiss Federal Institute of Technology (EPFL) in Lausanne, Switzerland, is looking to fill a fully funded PhD position. The qualified candidate will benefit from joining a very dynamic and multidisciplinary group working at the interface of computational neuroscience, neuroengineering, prosthetics and biology. EPFL provides state-of-the-art facilities and is one of the leading technical universities worldwide. PhD salaries at EPFL rank the highest in the world. Opportunity: The offered position will be based at the Defitech Center for interventional Neurotherapies (NeuroRestore) - a research and innovation center joining EPFL's lab of Prof. Gregoire Courtine and the University Hospital of Lausanne (CHUV) lab of Prof. Jocelyne Bloch. NeuroRestore conceives, develops and applies medical therapies aimed to restore neurological functions. To this end, NeuroRestore integrates implantable neurotechnologies with innovative treatments developed through rigorous preclinical and clinical studies. By working with our network of vibrant high-tech start-ups and established medical technology companies, NeuroRestore is committed to validate our medical therapy concepts. The overarching goal of NeuroRestore is to see our medical therapies used every day in hospitals and rehabilitation clinics worldwide. Description: A therapy based on epidural electrical stimulation (EES) of the spinal cord can restore the ability to walk to people paralyzed by spinal cord injury. EES does this by recruiting sensory axons within dorsal spinal roots that enter the spinal cord between the vertebrae. Yet, clinically available electrode arrays used to deliver the EES were not designed to target individual spinal roots. Data-driven design of the electrode arrays has the potential to substantially improve the specificity of spinal EES and, therefore, dramatically improve the recovery of people with spinal cord injury. The efficacy of EES can be enhanced through computational algorithms capable of designing EES protocols that fully utilize the interaction between the electrode array and patient's anatomy. These two developments are critical for deployment of the EES-based therapy to clinics around the world to help millions of people suffering from spinal cord injury. We have created a computational pipeline capable of creating detailed computational models of spinal columns from CT, MRI and fMRI recordings. These hybrid models are composed of 3D finite element models (FEM) to characterize the electric current and potential in the spinal cord of individuals, and various abstractions of compartmental cable models and network models of spinal cord neuronal populations and their connections to calculate the effects of EES on the spinal networks and, in turn, the activation of muscles. This computational approach has the potential to optimize the efficacy of EES on a personalized basis, lead to novel superior electrode array designs, and further our understanding of the mechanisms by which spinal cord controls movement. The successful candidate will work to automatize our computational pipeline in order to make the described approaches useful in applied clinical practice. They will work on the development of efficient and robust computer vision algorithms to automatically segment medical imaging datasets. They will also further develop our computational pipeline to enable automatic definition of personalized EES stimulation protocols. Furthermore, they will implement a computational framework around our pipeline that can perform a large-scale and diverse sensitivity and uncertainty analysis. This framework will be critical to enhance the efficacy and explore possible novel applications of spinal cord EES. Prerequisites: - Master's Degree in Physics, Computer Science, Mathematics, Microengineering, Electrical Engineering or related - Proficiency in Python, Matlab and C++ - Experience with computer vision and / or other machine learning techniques - Good written and verbal skills in English Contact: Applications including a CV and a cover letter describing your background and interest should be sent to MachineLearningPhD.Courtine(a)gmail.com. Informal inquiries are welcome.

Aug. 9, 2020

Postdoc in machine learning at EPFL (Lausanne, Switzerland)

by Milekovic Tomislav

Postdoc position in the lab of Prof. Gregoire Courtine at EPFL (Lausanne, Switzerland) Machine learning techniques to develop and enhance clinical treatments based on electrical stimulation of the spinal cord Location: The laboratory of Prof. Gregoire Courtine at the Swiss Federal Institute of Technology (EPFL) in Lausanne, Switzerland, is looking to fill a fully funded postdoc position. The qualified candidate will benefit from joining a very dynamic and multidisciplinary group working at the interface of computational neuroscience, neuroengineering, prosthetics and biology. EPFL provides state-of-the-art facilities and is one of the leading technical universities worldwide. Postdoc salaries at EPFL rank the highest in the world. Opportunity: The offered position will be based at the Defitech Center for interventional Neurotherapies (NeuroRestore) - a research and innovation center joining EPFL's lab of Prof. Gregoire Courtine and the University Hospital of Lausanne (CHUV) lab of Prof. Jocelyne Bloch. NeuroRestore conceives, develops and applies medical therapies aimed to restore neurological functions. To this end, NeuroRestore integrates implantable neurotechnologies with innovative treatments developed through rigorous preclinical and clinical studies. By working with our network of vibrant high-tech start-ups and established medical technology companies, NeuroRestore is committed to validate our medical therapy concepts. The overarching goal of NeuroRestore is to see our medical therapies used every day in hospitals and rehabilitation clinics worldwide. Description: Therapies based on epidural electrical stimulation (EES) of the spinal cord can restore the ability to walk to people paralyzed by spinal cord injury, and alleviate gait deficits of people with Parkinson's disease. EES does this by recruiting sensory axons within dorsal spinal roots that enter the spinal cord between the vertebrae to increase the activation of the spinal motor pools that, in turn, move the muscles. Yet, the efficacy of the EES-based therapies relies on synchronizing users' movement intentions with the spatiotemporal stimulation protocols that reliably and accurately generate paralyzed movements. Due to the large state space of all the stimulation parameters (location, amplitude, frequency, etc.) efficacy of the therapy depends on the fast and accurate initialization of the stimulation protocols. As the patients use the stimulation, small movements of the array, as well as changes in spine position due to users' posture can reduce the usability of the stimulation. Stimulation efficacy can be enhanced by dynamically adjusting the stimulation protocols to changes to the way how to users' spinal cord reacts to stimulation. Finally, the functional use of the stimulation largely depends on the accurate timing of stimulation delivery. Machine learning approaches that infer users' intentions based on behavioral, physiological or neural recordings can vastly improve the synchronization between intended and therapy-supported movements and, therefore, play a critical role in achieving functional recovery of patients. While the current medical devices mostly support block-based stimulation protocols that remain constant for hundreds of milliseconds, upcoming devices will enable changes of stimulation at a millisecond resolution, thus opening a new field for machine learning approaches that exploit these capabilities. The successful candidate will work to develop, implement and apply machine learning algorithms and approaches to enhance EES-based therapies. Specifically, he will: * Design the mapping procedures for the generation of transfer functions that relate the continuously-controlled stimulation to the evoked muscle activity. * Develop algorithms that automatically adjust these transfer functions as the interaction between patients and their EES-based therapy evolves. * Implement machine learning techniques that utilize users' behavioral, physiological and neural signals to continuously synchronize the delivery of stimulation with the users' movement intentions. * Lead the team that develops machine learning methods to initialize and adjust block-based EES protocols. * Assist and oversee the development and implementation of machine learning methods that use inference of discrete motor events to synchronize block-based EES protocols with the users' intentions. By integrating well-equipped and expertly staffed rodent, non-human primate and clinical research facilities, NeuroRestore provides an ideal substrate for rapidly developing, integrating and clinically validating cutting-edge machine learning concepts within medical therapies, with the capacity to push successfully proven concepts into the technology transition phase. The successful candidate will have access to these animal platforms and will work within the framework of multiple NeuroRestore clinical trials with people with spinal cord injury and Parkinson's disease. They will benefit from the possibility of validating their concepts in animal experiments and implementing them within the therapies being tested in the clinical trials. Prerequisites: * Doctoral degree (PhD) * Proficiency in Python, Matlab and C++ * Strong background in quantitative data analysis * Experience with applying multiple machine learning techniques to behavioral, physiological, biological and/or neural datasets * Good written and verbal skills in English Contact: Applications including a CV and a cover letter describing your background and interest should be sent to tomislav.milekovic(a)epfl.ch<mailto:tomislav.milekovic@epfl.ch>. Informal inquiries are welcome.

Aug. 9, 2020

Postdoc position in computational modelling at EPFL (Lausanne, Switzerland)

by Milekovic Tomislav

Postdoc position in the lab of Prof. Gregoire Courtine at EPFL (Lausanne, Switzerland) Data-driven computational modelling to develop and enhance clinical treatments based on electrical stimulation of the spinal cord Location: The laboratory of Prof. Gregoire Courtine at the Swiss Federal Institute of Technology (EPFL) in Lausanne, Switzerland, is looking to fill a fully funded postdoc position. The qualified candidate will benefit from joining a very dynamic and multidisciplinary group working at the interface of computational neuroscience, neuroengineering, prosthetics and biology. EPFL provides state-of-the-art facilities and is one of the leading technical universities worldwide. Postdoc salaries at EPFL rank the highest in the world. Opportunity: The offered position will be based at the Defitech Center for interventional Neurotherapies (NeuroRestore) - a research and innovation center joining EPFL's lab of Prof. Gregoire Courtine and the University Hospital of Lausanne (CHUV) lab of Prof. Jocelyne Bloch. NeuroRestore conceives, develops and applies medical therapies aimed to restore neurological functions. To this end, NeuroRestore integrates implantable neurotechnologies with innovative treatments developed through rigorous preclinical and clinical studies. By working with our network of vibrant high-tech start-ups and established medical technology companies, NeuroRestore is committed to validate our medical therapy concepts. The overarching goal of NeuroRestore is to see our medical therapies used every day in hospitals and rehabilitation clinics worldwide. Description: Therapies based on epidural electrical stimulation (EES) of the spinal cord can restore the ability to walk to people paralyzed by spinal cord injury, and alleviate gait deficits of people with Parkinson's disease. EES does this by recruiting sensory axons within dorsal spinal roots that enter the spinal cord between the vertebrae. Yet, clinically available electrode arrays used to deliver the EES were not designed to target individual spinal roots. Data driven design of the electrode arrays has the potential to substantially improve the specificity of spinal EES and, therefore, dramatically improve the recovery of patients. Pre-operative planning and intra-operative assistance based on accurate models of the spine can de-risk the surgery needed to implant the electrode arrays and increase the efficacy of the EES-based therapies. The efficacy of EES can be further enhanced through computational algorithms capable of designing EES protocols that fully utilize the interaction between the electrode array and patient's anatomy. These developments are critical for deployment of the EES-based therapy to clinics around the world to help millions of people suffering from spinal cord injury and Parkinson's disease. We have created a computational pipeline capable of creating detailed computational models of individual persons' spinal columns from CT, MRI and fMRI scans. These hybrid models are composed of: * 3D finite element models (FEM) to characterize the electric current and potential in the spinal cord of individuals. * Compartmental cable models to characterize numerous axon pathways that distribute the information from the spinal cord to rest of the body. * Network models of spinal cord neuronal populations to calculate the effects of EES on the spinal networks and, in turn, the activation of muscles. This computational approach has the potential to optimize the efficacy of EES on a personalized basis, lead to novel superior electrode array designs, and further our understanding of the mechanisms by which spinal cord controls movement. The successful candidate will work to generate highly accurate models of individual human and animal model spines and apply those models to revolutionize the EES-based therapies. Specifically, he will: * Use spinal models to generate new spinal electrode array designs, develop procedures for pre-operative planning and intra-operative assistance, and create methods that determine stimulation protocols from the spinal models. * Lead the work to extend our lumbosacral spinal cord models to thoracic and cervical spinal regions. * Integrate data from ever-more accurate invasive and non-invasive medical imagining and physiology techniques to enhance the accuracy of spinal models. * Coordinate integration of new findings on spinal neuronal populations, spinal networks and spinal pathways into our spinal models. * Assist and oversee the development of tools to automatize the process of spinal model generation. By integrating well-equipped and expertly staffed rodent, non-human primate and clinical research facilities, NeuroRestore provides an ideal substrate for rapidly developing, integrating and clinically validating cutting-edge computational modeling concepts that support medical therapies, with the capacity to push successfully proven concepts into the technology transition phase. The successful candidate will have access to these animal platforms and will work within the framework of multiple NeuroRestore clinical trials with people with spinal cord injury and Parkinson's disease. They will benefit from the possibility of validating their concepts in animal experiments and implementing them within the therapies being tested in the clinical trials. Prerequisites: * Doctoral degree (PhD) * Proficiency in Python, Matlab and C++ * Experience with finite element models, compartmental cable models and neurobiomechanical models * Experience in NEURON and/or NEST * Experience in OpenSim, Mujoco or Webots * Good written and verbal skills in English Contact: Applications including a CV and a cover letter describing your background and interest should be sent to tomislav.milekovic(a)epfl.ch<mailto:tomislav.milekovic@epfl.ch>. Informal inquiries are welcome.

Aug. 9, 2020

Post-doctoral scientist position at the Steinmetz Lab, University of Washington, Seattle

by Nick Steinmetz

The Steinmetz Lab at the University of Washington in Seattle ( www.steinmetzlab.net) is seeking applications for a position as a post-doctoral scientist. The scientist will work on data analysis and modeling in order to advance our large-scale electrophysiology technologies and our understanding of distributed computation across the brain. The primary focus of the position is the ‘Neuropixels Ultra’ project, in which we are developing novel electrophysiological technologies in collaboration with colleagues at the Allen Institute for Brain Science and with Johns Hopkins University. In addition to work on that specific project, the scientist will have access to cutting-edge large-scale datasets that combine behavior, electrophysiology, imaging, and optogenetics, and will have the ability to work creatively on questions of their own choosing, including helping to guide novel experimental data collection as necessary. The position is funded for 3 years by an NIH BRAIN Initiative grant, and is eligible to start immediately. It is expected that the scientist will eventually be based in Seattle, but a remote start is negotiable considering health concerns related to the ongoing global pandemic. The successful candidate is expected to have a Ph.D. in neuroscience or in relevant quantitative fields such as physics, computer science, statistics, or mathematics, and is expected to have a strong background and interest in data analysis methods in neuroscience, including such approaches as signal processing, linear algebra, and dynamical systems. We are located at the University of Washington, and have close collaborations with the Computational Neuroscience Center, the Swartz Center for Theoretical Neuroscience, the UW Institute for Neuroengineering, and the Allen Institute for Brain Science, which together create a lively community of computationally-minded neuroscientists on and near campus. The Steinmetz Lab is committed to making neuroscience a more open and inclusive field, so we strongly encourage applications from individuals with non-traditional backgrounds or from underrepresented groups. To apply, please send a CV and a brief statement of your background and interest in the position to Nick Steinmetz at nick.steinmetz(a)gmail.com. Informal inquiries are welcome - please get in touch if you would like more information! We aim to start this position as soon as possible so please be in touch by Friday Aug 21.

Aug. 6, 2020

Free online workshop on spiking neural networks as universal function approximators, Aug 31-Sep 1

by Dan Goodman

We are holding a free, online workshop on new approaches to training spiking neural networks. Details and registration at: https://neural-reckoning.github.io/snn_workshop_2020/ The last years have seen many exciting new developments to train spiking neural networks to perform complex information processing. This online workshop brings together researchers in the field to present their work and discuss ways of translating these findings into a better understanding of neural circuits. Topics include artificial and biologically plausible learning algorithms and the dissection of trained spiking circuits toward understanding neural processing. Speakers: * *Sander Bohte* (CWI) * *Iulia M. Comsa* (Google) * *Franz Scherr* (TUG) * *Emre Neftci* (UC Irvine) * *Timothee Masquelier* (CNRS Toulouse) * *Claudia Clopath* (Imperial College) * *Richard Naud* (U Ottawa) * *Julian Goeltz* (Uni Bern) Please pass this message on to anyone you think would be interested. Many thanks, Dan Goodman and Friedemann Zenke

Aug. 6, 2020

Postdoctoral position in neuroscience model curation / reproducible research

by Andrew Davison

A position in neuroscience model curation and verification is available within the Neuroinformatics research group of the Paris-Saclay Institute of Neuroscience, as part of the Human Brain Project. The successful candidate will be responsible for verifying the reproducibility of models produced by the Human Brain Project and/or published through the EBRAINS research infrastructure. This will involve working with scientists from many institutions to ensure their code is usable by others, and that it reliably reproduces the results shown in associated publications. It is expected that the candidate will develop workflows to automate this verification process as much as possible. Applicants should have a PhD in neuroscience, computer science, library/information science, or a closely related discipline, preferably with experience of computational modelling. Experience of simulation tools often used in computational neuroscience, such as NEURON, NEST or Brian, would be of particular benefit; knowledge of computer programming (in Python, C++, MATLAB or other) is essential. Knowledge of French would be helpful but is not required, as English is the working language of the project. The Paris-Saclay Institute of Neuroscience (https://neuropsi.cnrs.fr/en/institute-home/) is a research unit of the CNRS and the Université Paris-Saclay, located in Gif sur Yvette, about 40 minutes from central Paris. The Neuroinformatics research group (https://neuropsi.cnrs.fr/en/icn-home/andrew-davison/ <https://neuropsi.cnrs.fr/en/icn-home/andrew-davison/>) develops novel informatics tools and approaches for neuroscience data sharing, modelling and simulation, and for brain-inspired computing. This post is funded by the Human Brain Project, an EU Flagship initiative in which over 100 partners work together to build a completely new information computing technology infrastructure for neuroscience and for brain-related research in medicine and computing. To apply, visit https://emploi.cnrs.fr/Offres/CDD/UMR9197-ANDDAV-005/Default.aspx <https://emploi.cnrs.fr/Offres/CDD/UMR9197-ANDDAV-005/Default.aspx> Informal inquiries to andrew.davison(a)cnrs.fr <mailto:andrew.davison@cnrs.fr> are welcome. Application deadline 17th August 2020

Aug. 5, 2020

Postdoctoral, predoctoral, and data scientist positions: causal inference

by Jan Drugowitsch

How do our brains interpret sensory inputs to determine how we move through the world? How do the objects we perceive in the environment shape these interpretations? And what happens when these objects are themselves moving? We will address these questions in a multi-institution project that examines neural and computation mechanisms of causal inference in the context of interactions between perception of object motion, self-motion, and depth. We are looking for talented and creative scientists to join our effort. We are offering multiple positions for postdoctoral fellows, graduate research assistants, and a data scientist as part of a BRAIN Initiative U19 project focused on the neural mechanisms of causal inference. This multi-institution project involves experimental and computational/theoretical laboratories at the University of Rochester (Drs. Greg DeAngelis and Ralf Haefner), Harvard Medical School (Dr. Jan Drugowitsch), New York University (Dr. Dora Angelaki), Baylor College of Medicine / Rice University (Dr. Xaq Pitkow), and the University of Washington (Dr. Greg Horwitz). The project is highly collaborative and interactive, involving high-density electrophysiological recordings in both macaque monkeys and mice, optogenetic manipulations in monkeys, development of novel dynamical theoretical frameworks, application of high-dimensional neural analyses, and establishment of a data analysis and storage platform across laboratories. For the theoretical/computational efforts, candidates are expected to have a background in a quantitative field like computational neuroscience, machine learning, reinforcement learning, statistics, physics, etc.. For the experimental efforts, candidates are expected to have a background in neuroscience, experimental psychology, biomedical engineering, or a related field, and previous research experience in systems neuroscience is highly beneficial. Successful candidates will work primarily at one of the participating institutions but will collaborate broadly with team members across institutions. Candidates should submit their CV, a statement of research interests, and the names and contact information for three references. Please consult https://www.indeed.com/job/postdoctoral-predoctoral-and-data-scientist-posi… for further details, and for how to apply.

Aug. 5, 2020

Postdoctoral positions in computational neuroscience, sleep, and developmental neurophysiology

by Blumberg, Mark S

We are seeking to fill multiple positions for highly skilled and motivated postdoctoral scholars. Successful applicants will learn to perform experiments in neurophysiology and sleep, as well as develop computational models based on years of spike and LFP data collected in infant rats and, more recently, human infants. We have developed state-of-the-art multisite recording methods to detect neural activity in cortex, hippocampus, thalamus, cerebellum, and brainstem in developing rats and relate that activity to changes in sleep-wake states and limb movements (captured using high-speed video and tracked using DeepLabCut). Unique opportunities abound for analyzing and modeling these complex datasets to reveal functional changes in neural activity across the first several postnatal weeks. Among other things, we are investigating the integration of corollary discharge and reafferent signals, the role of state-dependent neuromodulation, and the contributions of neural oscillations to the emergence of functionally connected brain regions. Please visit the lab’s homepage<https://psychology.uiowa.edu/blumberg-lab> and read our recent papers<https://psychology.uiowa.edu/blumberg-lab/articles> to gain a better sense of the kinds of questions we ask and the methods we use to answer them. You can learn more about the PI, Mark Blumberg, here<https://psychology.uiowa.edu/sites/psychology.uiowa.edu/files/groups/blumbe…> and here<https://psychology.uiowa.edu/sites/psychology.uiowa.edu/files/groups/blumbe…>. The laboratory is located in the Department of Psychological and Brain Sciences<https://psychology.uiowa.edu/> at the University of Iowa<https://uiowa.edu/>, a leading public university. The university is located in Iowa City, a midsize community with a high quality of life. The department is experiencing a period of enhanced growth and investment, including completion of a new building<https://psychology.uiowa.edu/new-home-department-psychological-and-brain-sc…>. In addition, the University of Iowa is home to a large and vibrant neuroscience community, highlighted by the Iowa Neuroscience Institute<https://medicine.uiowa.edu/iowaneuroscience/> that brings together over 100 neuroscientists across 26 departments. In addition to research opportunities, successful applicants will have opportunities to teach courses, mentor graduate and undergraduate students, write grants, and engage in activities to enhance professional development and prepare for an academic career. Salary is negotiable, but is expected to be above NIH's minimum guidelines for postdocs. Requirements: A PhD with experience in computational and/or behavioral neuroscience. Experience with MATLAB (or other programming languages) is required. Please send a letter of interest, curriculum vitae, and selected publications to Mark Blumberg at mark-blumberg(a)uiowa.edu<mailto:mark-blumberg@uiowa.edu>. _________________________________ Mark S. Blumberg, Ph.D. F. Wendell Miller Professor Department Chair Psychological & Brain Sciences Iowa Neuroscience Institute The University of Iowa _________________________________

Aug. 4, 2020