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Postdoc in computational cognitive neuroscience of speech processing at the University of Texas at Austin (USA)
by Hamilton, Liberty Aug. 29, 2020

Aug. 29, 2020

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…

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Professor position in Goettingen, Germany
by Rizzoli, Silvio Aug. 26, 2020

Aug. 26, 2020

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)

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Professorship in computational modeling of synaptic physiology at the University Medical Center Göttingen, Germany
by Erik De Schutter Aug. 26, 2020

Aug. 26, 2020

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…

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Postdoc/PhD/RA positions at ÖAW Acoustics Research Institute, Vienna
by Robert Baumgartner Aug. 19, 2020

Aug. 19, 2020

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>

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Phd/Postdoc position for research on interpretable Deep Learning and brain modelling
by Wolfgang Maass Aug. 18, 2020

Aug. 18, 2020

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

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Neurodata Without Borders (NWB) User Days Workshop - September 21-23
by Ryan Ly Aug. 17, 2020

Aug. 17, 2020

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

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Neural Data Analysis Online Course
by Maryam Aug. 17, 2020

Aug. 17, 2020

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

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One Fellow position for closed-loop systems in neuroengineering @IIT Genova, ITALY
by Michela Chiappalone Aug. 17, 2020

Aug. 17, 2020

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>

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Web Conference /DMSE 2020, Denmark - Deadline Approaching[Third Batch Call for Submissions] .
by Fredrick Johnson Aug. 13, 2020

Aug. 13, 2020

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.

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Post-doctoral Research position at Johns Hopkins BME
by Adam Charles Aug. 11, 2020

Aug. 11, 2020

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.

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