- Comp-neuro - lists.cnsorg.org

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