May 2020 - Comp-neuro - lists.cnsorg.org

International Symposium on Artificial Intelligence and Brain Science 2020 in Tokyo

by Kenji Doya

We are pleased to announce a symposium that brings together researchers in the forefront of AI and neuroscience. Application for poster presentation is open till June 1st and general registration will start from July 1st, 2020. ******** International Symposium on Artificial Intelligence and Brain Science Date: October 10-12 (Sat-Mon), 2020 Venue: Ito Hall, The University of Tokyo, Japan Web site: http://www.brain-ai.jp/symposium2020/ Program (it may be held online depending on the COVID-19 situation) • Saturday, October 10th (from 1pm) Keynote: Josh Tenenbaum (MIT) Session 1: Deep Learning and Reinforcement Learning Yann LeCun (NYU, Facebook), Yutaka Matsuo (U Tokyo), Doina Precup (McGill U) David Silver (DeepMind), Masashi Sugiyama (RIKEN AIP/U Tokyo IRCN) • Sunday, October 11th Session 2: World Model Learning and Inference Ila Fiete (MIT), Karl Friston (UCL), Yukie Nagai (U Tokyo IRCN) Maneesh Sahani (Gatsby Unit), Tadahiro Taniguchi (Ritsumeikan U) Poster Session Session 3: Metacognition and Metalearning Matthew Botvinick (DeepMind), Ryota Kanai (ARAYA), Angela Langdon (Princeton U) Hiroyuki Nakahara (RIKEN CBS), Xiao-Jing Wang (NYU) • Monday, October 12th Session 4: AI for Neuroscience and Neuromorphic Technologies Jim DiCarlo (MIT), Yukiyaku Kamitani (Kyoto U), Rosalyn Moran (King’s College London) Terry Sejnowski (Salk Institute), Hidehiko Takahashi (Tokyo MDU) Session 5: Social Impact and Neuro-AI Ethics Anne Churchland (CSHL), Kenji Doya (OIST), Arisa Ema (U Tokyo IFI) Hiroaki Kitano (SONY CSL), Stuart Russell (UC Berkeley) Poster Registration: May 11th - June 1st (review result notified by June 20) General Registration: July 1st - 31st (or when the capacity is reached) Please check the web site: http://www.brain-ai.jp/symposium2020/ Sponsor: KAKENHI Project on Artificial Intelligence and Brain Science (http://www.brain-ai.jp) Co-sponsors: RIKEN AIP (https://aip.riken.jp/?lang=en) U Tokyo IFI (https://ifi.u-tokyo.ac.jp/en/) U Tokyo IRCN (https://ircn.jp/en/) Contact: ncus(a)oist.jp ---- Kenji Doya <doya(a)oist.jp> Neural Computation Unit, Okinawa Institute of Science and Technology Graduate University 1919-1 Tancha, Onna, Okinawa 904-0495, Japan Phone: +81-98-966-8594; Fax: +81-98-966-2891 https://groups.oist.jp/ncu

May 12, 2020

Dual-award PhD in Neuromorphic Olfaction (UH/WSU)

by Michael Schmuker

We’re looking for an outstanding candidate to enter a Dual-PhD programme on Neuromorphic Olfaction. The successful candidate will pursue a PhD in the BioMachineLearning lab at the University of Hertfordshire (UK), and the International Centre for Neuromorphic Systems (ICSC), at Western Sydney University, Australia. The successful candidate will develop event-based algorithms for Neuromorphic olfaction, in close collaboration with Neuromorphic hardware developers and computational neuroscientists. In this unique opportunity, the candidate is eligible to obtain a PhD award from both universities, UH and WSU. It’s a fully funded programme that covers also travel expenses between UH and WSU. Interested? Get in touch! More info: https://biomachinelearning.net/positions <https://biomachinelearning.net/positions> — Dr Michael Schmuker AFHEA Reader in Data Science Biocomputation Group University of Hertfordshire https://biomachinelearning.net <https://biomachinelearning.net/>

May 12, 2020

[jobs] Postdoctoral position in: Cognition for safety and ergonomics for human-robot interaction in manufacturing workspaces - [ Postdoc ]

by Francesco Rea

The COgNiTive Architecture for Collaborative Technologies Research Line at Istituto Italiano di Tecnologia in Genoa - Center for Human Technologies, is opening one postdoctoral position focusing on the assessment of engagement with safety and ergonomic work, design of proactive measures and counter measures for safety preservation, optimization of ergonomics, design of software infrastructure for social interaction and safety-ergonomic preservation. The research activities will be carried out under the supervision of Dr. Alessandra Sciutti and Dr. Francesco Rea (Participant Manager within the Steering Board -SB). The selected candidate will work with a multidisciplinary consortium of participant to the awarded project APRIL (H2020, n. G.A. 870142). APRIL project aims at implementing and deploying market oriented, low cost and multipurpose robots that supports semiautomatic tasks in manufacturing production lines and handle flexible or deformable materials in industries of any size or domain. The candidate will work with different robotic platforms for human-robot interaction in manufacturing context selected together with the consortium and with different sensorial setups specifically designed to assess the gaze pattern, reaction time, working movements, fatigue level in workers. https://www.iit.it/careers/openings/opening/1198-postdoctoral-position-in-c… The activity will be carried out within the implementation of the APRIL project in a fully equipped laboratory, including motion capture and eye-tracking devices, and in a team with a variety of competences ranging from robotics to designing of experimental studies on humans. The research for this Postdoctoral position will involve: * Sensing of human working conditions for social, safe and ergonomic based activities on human-robot collaboration * Design of control systems for Safety in human-robot collaboration * Development of a system improving human-robot social collaboration in manufacturing context * Development of a system improving ergonomics and mitigating health and safety risks The first goal of the research is redesigning cognition for safety and ergonomics for human-robot collaboration in manufacturing workspaces. The second goal of technological implications is to support the design of new multipurpose robotics for manipulation of deformable materials in manufacturing processes. The successful candidate will be offered a salary commensurate to experience and skills. Interested applicants should submit CV, list of publications, name and contacts of 2 referees and a statement of research interest to jobpost.78417(a)iit.it<mailto:jobpost.78417@iit.it> by May 20, 2020 quoting Postdoctoral position in "Cognition for safety and ergonomics for human-robot interaction in manufacturing workspaces - CB 78417" in the subject line.

May 10, 2020

Research Assistant (Research Associate)

by Artur Luczak

Research Assistant (Research Associate) at the University of Lethbridge. Our lab seeks a highly motivated individual with a strong computational background to work at the interface of neuroscience and machine learning. We are especially interested in the application of deep neural networks to improve our understanding of neuronal data, and the application of recent neuroscience findings to improve the performance of deep neural networks. In our lab we also record the activity of hundreds of neurons in normal and epileptic animals, and the successful candidate is welcome to participate in those projects. Preferred candidate should have strong background in modeling neurons and networks, and should be very familiar with models of synaptic plasticity (e.g. BCM). Our lab at the Canadian Centre for Behavioural Neuroscience in the University of Lethbridge is located in the sunniest area of Canada and next to scenic Rocky Mountains. All qualified candidates are encouraged to apply; however, Canadians and permanent residents will be given priority. For more information please visit our website at: http://lethbridgebraindynamics.com/artur_luczak. Duties and responsibilities: - Conduct research and literature reviews, in related topics for use in scholarly publications. - Compile research results and assist professors in the analysis of results and the preparation of journal articles or papers. - Analyze data using statistics, programming techniques and artificial neural networks. - Develop analytical methods in biologically plausible artificial neural networks. - Participate in scientific discussion groups and seminars. All duties and responsibilities shall be carried out under the supervision of Prof. Artur Luczak. Skills & Qualifications: - Master degree’s in neuroscience, physics, computer science or related topics. - 3 years of related research work experience. - Having strong background in modeling neurons and networks, and should be very familiar with models of synaptic plasticity. - Familiarity with epilepsy is an asset - Highly experienced in programming with Python and Matlab. - Ability to work with data, applying machine learning methods and deep learning. - Working with computer clusters and cloud computing. - Excellent communication and team work skills. Working Conditions: Work in an office with a computer provided. Hours of Work: Full-time, 30-32 hours per week. Salary: $21.63 /hour Duration: one year with the opportunity for extension Language requirements: English Benefits: Standard benefits including Extended Health, Dental, EFAP, Life Insurance and Long Term Disability. Interested applicants are encouraged to contact Professor Artur Luczak (Luczak(a)uleth.ca) with a copy of their CV and a cover letter. Address: University of Lethbridge. 4401 University Drive West, Lethbridge, Alberta T1M 1R6, Canada

May 9, 2020

Call for papers MLCN at MICCAI 2020

by MLCN Workshop

*Please find below the call for papers for the International Workshop of Machine Learning in Clinical Neuroimaging (MLCN) on 4 October 2020 at MICCAI 2020 in Lima, Peru. We welcome contributions on novel machine learning methods and their applications to clinical neuroimaging data.* The submission deadline is *30 June 2020*, and all MLCN accepted papers will be eligible for the best paper award of 500 USD. For more information, please visit https://mlcnws.com/. Best wishes, The MLCN 2020 committee Christos Davatzikos Andre Marquand Jonas Richiardi Emma Robinson Ahmed Abdulkadir Cher Bass Mohamad Habes Seyed Mostafa Kia Jane Maryam Rondina Chantal Tax Hongzhi Wang Thomas Wolfers International Workshop on Machine Learning in Clinical Neuroimaging 4 October 2020 in Lima, Peru The International Workshop of Machine Learning in Clinical Neuroimaging ( https://mlcnws.com/) a satellite event of MICCAI (https://miccai2020.org) calls for original papers in the field of clinical neuroimaging data analysis with machine learning. The two tracks of the workshop include methodological innovations as well as clinical applications. This highly interdisciplinary topic provides an excellent platform to connect researchers of varying disciplines and to collectively advance the field in multiple directions. For the machine learning track, we seek contributions with substantial methodological novelty in analyzing high-dimensional, longitudinal, and heterogeneous neuroimaging data using stable, scalable, and interpretable machine learning models. Topics of interest include but are not limited to: - Spatio-temporal brain data analysis - Structural data analysis - Graph theory and complex network analysis - Longitudinal data analysis - Model stability and interpretability - Model scalability in large neuroimaging datasets - Multi-source data integration and multi-view learning - Multi-site data analysis, from preprocessing to modeling - Domain adaptation, data harmonization, and transfer learning in neuroimaging - Unsupervised methods for stratifying brain disorders - Deep learning in clinical neuroimaging - Model uncertainty in clinical predictions - ... In the clinical neuroimaging track, we seek contributions that explore how the application of advanced machine learning methods help us to move towards precision medicine for complex brain disorders. Topics of interest include but are not limited to: - Biomarker discovery - Refinement of nosology and diagnostics - Biological validation of clinical syndromes - Treatment outcome prediction - Course prediction - Analysis of wearable sensors - Neurogenetics and brain imaging genetics - Mechanistic modeling - Brain aging - ... Submission Process: The workshop seeks high quality, original, and unpublished work that addresses one or more challenges described above. Papers should be submitted electronically in Springer Lecture Notes in Computer Science (LCNS) style (see https://www.miccai2020.org/en/PAPER-SUBMISSION-GUIDELINE.html#manuscript-fo… for detailed author guidelines) using the CMT system at https://cmt3.research.microsoft.com/MLCN2020. The page limit is 8-pages (text, figures, and tables) plus up to 2-pages of references. We review the submissions in a double-blind process. Please make sure that your submission is anonymous. Accepted papers will be published in a joint proceeding with the MICCAI 2020 conference. Best Paper Award: This year, all MLCN accepted papers will be eligible for the best paper award. The recipient of the award will be chosen by the MLCN scientific committee based on the scientific quality and novelty of contributions. The winner will be announced at the end of the workshop and will receive 500 USD honorarium. Important Dates: - Paper submission deadline: June 30th, 2020 - Notification of Acceptance: July 24th, 2020 - Camera-ready Submission: July 31st, 2020 - Workshop Date: 4 October 2020

May 8, 2020

PhD position at EPFL (Lausanne, Switzerland)

by Milekovic Tomislav

PhD position in the lab of Prof. Gregoire Courtine at EPFL (Lausanne, Switzerland) A computational approach to personalize and optimize clinical treatments for restoring locomotion to people paralyzed by spinal cord injury 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. In addition, PhD salaries at EPFL rank the highest in the world. 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 could 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 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 individual persons' spinal columns from MRI scans 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 Finite Element Models, compartmental cable models, neurobiomechanical models and Machine Learning Contact: Applications including a CV and a cover letter should be sent to andreas.rowald(a)epfl.ch

May 7, 2020

Fully-funded PhD Positions in Biomedical Imaging Computing, Lehigh University, USA

by Yu Zhang

*Fully-funded PhD Positions in Biomedical Imaging Computing, Lehigh University, USA* The Biomedical Imaging and Computation Laboratory (BIC Lab) at Lehigh University (Bethlehem, USA) is currently accepting applications for PhD positions in biomedical imaging computing. *Position description: *The research project will focus on developing and applying a variety of machine learning/deep learning methods for biomedical imaging analysis and prediction. The successful applicants will contribute to research and development in artificial intelligence (AI)-driven neural biomarker discovery using advanced neuroimaging techniques (e.g., MRI and EEG) for precision medicine of brain diseases. *Qualifications: *Successful applicants will have a Bachelor’s or Master’s degree in Electrical Engineering, Biomedical Engineering, Computer Science, or other related fields, and strong motivation to do high-quality research in novel biomedical imaging computing with machine learning. Additionally, strong quantitative skills and a programming background (Matlab and/or Python) are important. Previous research experiences in related fields and scientific publications in journals/conferences are preferred. *Lehigh University: *Lehigh University is a premier research university in the United States, consistently ranked in the top tier of national research universities each year. It is a coeducational, nondenominational, private university that offers a distinctive academic environment for undergraduate and graduate students from across the globe. Located in Pennsylvania’s scenic Lehigh Valley, the campus is situated on 1,600 acres in close proximity to both New York City and Philadelphia. The application materials will be reviewed immediately once received. The expected starting time is Fall 2020 or Spring 2021. Interested candidates should send a cover letter describing your research interests, CV (including undergraduate GPA and ranking, transcripts if applicable), and contact information of three references to Dr. Yu Zhang at yzhangsu(a)stanford.edu and zhangyubrain(a)gmail.com. Check here for detailed admission requirements (e.g., GRE and TOEFL scores) https://engineering.lehigh.edu/bioe/graduate/bioengineering-admissions-guid… -- Yu Zhang, PhD Assistant Professor Department of Bioengineering, Lehigh University Research Fellow Wu Tsai Neurosciences Institute, Stanford University Webpage: http://yuzhangresearch.weebly.com/ Email: yzhangsu(a)stanford.edu

May 7, 2020

Neuromatch 2.0 – an online unconference in neuroscience, May 25-27, 2020

by Grace Lindsay

Announcing Neuromatch 2.0 – an online unconference in neuroscience, May 25-27, 2020. After a successful two-day conference in March, Neuromatch is back for three days this May! This fully online computational neuroscience conference will include invited and submitted talks, a virtual poster session, panels, debates, group discussion, and “mind-matching” events. In partnership with the IEEE Brain Initiative, one day of the conference will focus on neural data analysis. In partnership with eLife, Neuromatch will also feature talks and discussion with early career researchers. Registration and talk/poster submissions are due May 20th. For instructions on how to submit, please see: https://neuromatch.io/instructions/ For more information and to register for the conference, visit our site: https://neuromatch.io/ If you want to participate in the “mind-matching” or group discussion events (where you’ll be algorithmically paired with other scientists based on mutual research interests), don’t forget to include example abstracts so we know your interests! Confirmed Speakers: - Eve Marder (Brandeis University) - Jenny Bizley (UCL) - Richard Naud (U of Ottawa) - Yael Niv (Princeton) - Jakob Macke (TU of Munich) - Asya Rolls (Technion) - Joseph Raimondo (U of Cape Town) - Dan Yamins (Stanford) - Demba Ba (Harvard) - Ilana Witten (Princeton) - Eugenia Chiappe (Champalimaud) - Byron Yu (CMU) - Adrienne Fairhall (U of Washington) - Polina Anikeeva (MIT) Sincerely, Neuromatch 2.0 organizers Dan Goodman (Imperial) Konrad Kording (UPenn) Brad Wyble (Penn State) Titipat Achakulvisut (UPenn) Tim Vogels (Oxford) Chris Rozell (Georgia Tech) Yiota Poirazi (IMBB/FORTH) Grace Lindsay (UCL)

May 7, 2020

Two Post-doc opportunities in machine learning for neurological conditions

by Andre Altmann

Dear Comp-Neuro Community, If you like the challenge of developing novel computational and mathematical solutions to real-world medical challenges, then you will love working with us in the UCL Centre for Medical Image Computing (CMIC) [1] in London, UK. We are looking for two excellent postdoctoral researchers to join the UCL POND[2] team's well-established, supportive, expanding team of computational scientists in developing state-of-the-art data-driven solutions for neurological conditions like Alzheimer’s disease. In this field we lead international initiatives such as EuroPOND[3], TADPOLE Challenge[4], and the new E-DADS consortium[5]. Importantly, we ensure impact by collaborating closely with clinical scientists on our doorstep in London and our network of collaborators across the globe. *Deadline for applications is 25th May*. For details on the roles and to apply: http://pond.cs.ucl.ac.uk/vacancies Best wishes, Andre Altmann Links: [1] https://www.ucl.ac.uk/medical-image-computing/ [2] http://pond.cs.ucl.ac.uk/ [3] http://europond.eu/ [4] https://tadpole.grand-challenge.org/ [5] https://e-dads.github.io/

May 7, 2020

Postdoctoral Associate in Computational Neuroscience of Learning and Memory

by Yuri Dabaghian

*Postdoctoral Associate in Computational Neuroscience of Learning and Memory* A NSF-funded postdoctoral position is immediately available in the group of Dr. Yuri Dabaghian at the University of Texas in Houston. The goal of the projects is to understand the hippocampal mechanisms of learning and memory using computational topological and geometric approaches, neuronal network theory. The projects involve both computational modeling and analyses of in-vivo electrophysiological data provided by experimental collaborators. The ideal candidate should have a strong in background Computational Neuroscience, Applied Mathematics, Theoretical Physics or similar. To apply, please send a CV, letter of motivation and the names of two-three references to Yuri Dabaghian (Yuri.A.Dabaghian(a)uth.tmc.edu) Applications will be accepted until the position is filled.

May 4, 2020