- Comp-neuro - lists.cnsorg.org

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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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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