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PhD position in machine learning at EPFL (Lausanne, Switzerland)
by Milekovic Tomislav 09 Aug '20

09 Aug '20

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.

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Postdoc in machine learning at EPFL (Lausanne, Switzerland)
by Milekovic Tomislav 09 Aug '20

09 Aug '20

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.

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Postdoc position in computational modelling at EPFL (Lausanne, Switzerland)
by Milekovic Tomislav 09 Aug '20

09 Aug '20

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.

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Post-doctoral scientist position at the Steinmetz Lab, University of Washington, Seattle
by Nick Steinmetz 06 Aug '20

06 Aug '20

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.

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Free online workshop on spiking neural networks as universal function approximators, Aug 31-Sep 1
by Dan Goodman 06 Aug '20

06 Aug '20

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

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Postdoctoral position in neuroscience model curation / reproducible research
by Andrew Davison 05 Aug '20

05 Aug '20

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

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Postdoctoral, predoctoral, and data scientist positions: causal inference
by Jan Drugowitsch 05 Aug '20

05 Aug '20

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.

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Postdoctoral positions in computational neuroscience, sleep, and developmental neurophysiology
by Blumberg, Mark S 04 Aug '20

04 Aug '20

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 _________________________________

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Submit Now - Call for Research Projects | Bitbrain
by Eduardo López-Larraz 04 Aug '20

04 Aug '20

Call for Research Projects Bitbrain grants up to 150K€ of equipment for Research Projects in the areas of human behavior and brain-computer interfaces. Aim of the Call This year has been unexpectedly complicated for all of us. We have had an ongoing threat to public health with a strong impact on economic activity in every country and organization. Scientific endeavors have been delayed in most research fields, and many research conferences and symposiums have been delayed or canceled. The aim of this call is to catalyze and mobilize research in human behavior and brain-computer interfaces during this difficult time, by supporting outstanding research projects for 2 years of duration starting in 2021. This is our way of giving back to the scientific community and helping research projects start-up in the wake of COVID 19. Bitbrain's mission is to boost real-world research and real-life applications that enhance people's lives. For this reason, we particularly welcome project proposals from academia or research institutions that are exploring neuroscience research in real-world settings. The deadline for submissions is September 21st, 2020. More info here: https://www.bitbrain.com/call-for-projects-2020 <https://urldefense.com/v3/__https://www.bitbrain.com/call-for-projects-2020…> The Awards This initiative awards projects with up to a total of 150K€ in Bitbrain equipment plus software licenses. Bitbrain systems are specifically designed to facilitate research in real-world settings and include high-tech EEG brain sensing devices and 35+ complementary monitoring technologies, as well as human behavior research software solutions for synchronized multimodal data acquisition and analysis. Call topics 1. Topic 1: Human Behavior Research: We are looking for projects that expand the understanding of human behavior using multimodal approaches, preferably in ecological and natural conditions. The research includes, but is not limited to, cognitive sciences, clinical research, education, developmental psychology, human performance, UX or human factors, neuroeconomics, hyperscanning, neuroaesthetics, etc. Awards: According to the research needs of the project, the award may include the following: 1) Hardware: dry/semi-dry EEG systems in combination with a wide range of biosensors (GSR, BVP, ExG, etc) specifically developed to carry out real-world research, while favoring signal quality, usability, set up time, and user comfort. 2) Software: SDK for real-time I/O and third-party data processing (LSL, Matlab, Python, BCI200, etc); SennsLab software for synchronized data collection of 35+ types of biosensors and other research modalities including eye-tracking, cameras, microphones, etc, and other 3rd party options such as E-prime; SennsMetrics software for computation and data analysis of behavioral metrics. 1. Topic 2: Brain-Computer Interfaces (BCI): We are looking for projects that expand the introduction or usage of brain-computer interfaces in ecological and natural conditions. Research project proposals should focus on the application of brain-computer interfaces in real-world scenarios, in areas such as immersive environments, neurorehabilitation, neuroadaptive technologies, hyperscanning, neuroprosthetics, etc. Awards: According to the research needs of the project, the award may include the following: 1) Hardware: Mobile EEG systems for brain-computer interfaces including dry-EEG devices and semi-dry EEG systems (8, 16, 32 and 64ch), with a very quick and easy setup and comfortable interface. 2) Software: programming tools with great compatibility with real-time I/O and third-party data processing (LSL, Matlab, Python, BCI2000, OpenVibe, etc). Jury 1. Alan Kingstone, Ph.D. <https://psych.ubc.ca/profile/alan-kingstone/>, Professor, Department of Psychology, University of British Columbia. Canada. 2. Ander Ramos, Ph.D. <https://www.ramoslab.org/>, Head of the Neurorehabilitation Lab. University of Tübingen. Germany. 3. Dima Amso, Ph.D. <https://www.brown.edu/academics/cognitive-linguistic-psychological-sciences…>, Professor, Department of Psychology at Columbia University. USA. 4. Gernot Müller-Putz, Ph.D. <https://www.tugraz.at/institutes/ine/people/gernot-mueller-putz/>, Professor and Head of the Institute of Neural Engineering. Graz University of Technology. Austria. 5. Marco Congedo, Ph.D. <https://www.gipsa-lab.grenoble-inp.fr/page_pro.php?vid=518>, Charge de Research (CNRS), France. 6. Tad Brunye, Ph.D <https://ase.tufts.edu/psychology/spacelab/members.htm>., Visiting Associate Professor, Department of Psychology, Tufts University; Senior Research Psychologist, Army Natick Soldier Research, Development, and Engineering Center, USA. 7. Tiago Falk, Ph.D. <http://musaelab.ca/team-view/prof-tiago-h-falk/>, Associate Professor and Multimedia/Multimodal Signal Analysis and Enhancement (MuSAE) Laboratory. University of Toronto. Canada. Important Dates - Submission of applications: 21st September - Communication of the awards: 6th October - Contracts: 12th October - Equipment delivering: 20th December - Kick-off Meetings: 11th January More info here: https://www.bitbrain.com/call-for-projects-2020 <https://urldefense.com/v3/__https://www.bitbrain.com/call-for-projects-2020…>

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Bernstein Conference 2020 – registration now open
by a.dorrn＠fz-juelich.de 04 Aug '20

04 Aug '20

Each year the Bernstein Network invites the international computational neuroscience community to the annual Bernstein Conference for intensive scientific exchange. It has established itself as one of the most renown conferences worldwide in this field, attracting students, postdocs and PIs from around the world to meet and discuss new scientific discoveries. This year’s Bernstein Conference will take place September 29 - October 1, 2020 and will be held entirely online for the first time. It will be followed by the online Bernstein PhD Symposium on October 2, 2020. www.bernstein-conference.de<https://bit.ly/BC_online> ____ NEWS · Registration is now open: bit.ly/BC20_Registration<https://bit.ly/BC20_Registration> · Participation will be free of charge · Abstract submission deadline is approaching on August 21 Please find more information here: bit.ly/BC20abstracts<https://bit.ly/BC20abstracts> · Program for online Bernstein PhD symposium is now online: bit.ly/bc20_phd<https://bit.ly/bc20_phd> ____ INVITED SPEAKERS William Bialek (Princeton University, USA) Michael Brecht (Humboldt Universität zu Berlin, Germany) Laura Busse (LMU Munich, Germany) Megan Carey (Champalimaud Center for the Unknown, Lisbon, Portugal) Rosa Cossart (Institut de neurobiologie de la méditerrané, Marseille, France) Ann Hermundstad (Janelia Research Campus, Ashburn, USA) Roozbeh Kiani (New York University, New York City, USA) Vanessa Ruta (The Rockefeller University, New York City, USA) David Sussillo (Google AI, Mountain View, USA) Srdjan Ostojic (L'école normale supérieure, Paris, France) Fred Wolf (Max-Planck Institute for Dynamics and Self-Organization, Göttingen, Germany) ____ CONFERENCE COMMITTEE Udo Ernst (Conference Chair) Elad Schneidmann (Program Chair) Julijana Gjiorgjieva (Program Vice Chair) Tim Vogels (Workshop Chair) Marion Silies (Workshop Vice Chair) Henning Sprekeler (Conference Host) Danielle Bassett, Matthew Botvinick, Peter Dayan, Alain Destexhe, Tim Gollisch, Stephanie Palmer, Nathalie Rochefort, Nicole Rust, Gasper Tkacik ____ For any further questions, please contact: bernstein.conference(a)fz-juelich.de ------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------ Forschungszentrum Juelich GmbH 52425 Juelich Sitz der Gesellschaft: Juelich Eingetragen im Handelsregister des Amtsgerichts Dueren Nr. HR B 3498 Vorsitzender des Aufsichtsrats: MinDir Volker Rieke Geschaeftsfuehrung: Prof. Dr.-Ing. Wolfgang Marquardt (Vorsitzender), Karsten Beneke (stellv. Vorsitzender), Prof. Dr.-Ing. Harald Bolt ------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------

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