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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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PhD position on human resource rationality
by Dominik Endres Aug. 11, 2020

Aug. 11, 2020

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

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Senior Scientist – Stroke position at the NeuroRestore center (Lausanne, Switzerland)
by Milekovic Tomislav Aug. 9, 2020

Aug. 9, 2020

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

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PhD position in machine learning at EPFL (Lausanne, Switzerland)
by Milekovic Tomislav Aug. 9, 2020

Aug. 9, 2020

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 Aug. 9, 2020

Aug. 9, 2020

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 Aug. 9, 2020

Aug. 9, 2020

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 Aug. 6, 2020

Aug. 6, 2020

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 Aug. 6, 2020

Aug. 6, 2020

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 Aug. 5, 2020

Aug. 5, 2020

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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