- Comp-neuro - lists.cnsorg.org

INCF/OCNS Software WG Dev session on SciUnit/NeuronUnit (June 21, 1600 UTC)

by Ankur Sinha

Dear all, Apologies for the cross posts. The INCF/OCNS Software WG[1] is happy to announce the next Dev Session where members of the developer teams of commonly used neuroscience software introduce their software and then discuss their *development* pipelines. ---- In the next Dev Session, Rick Gerkin will introduce the SciUnit framework and discuss its development: https://ocns.github.io/SoftwareWG/2021/06/04/dev-session-rick-gerkin-sciuni… The session will be held at June 21, 2021, 1600 UTC over Zoom: https://ucl.zoom.us/j/91246735902?pwd=WVdVK3I5SGdlaDFZdllDQ0w2djVBdz09 Click here to see the event in your local time: https://www.timeanddate.com/worldclock/fixedtime.html?msg=Dev+session%3A+Ri… SciUnit is a discipline-agnostic framework for model validation, handling all of the testing workflow by using a implementation-independent interface to models. SciUnit also contains code for visualization of model results, and command line tools for incorporating testing into continuous integration workflows. SciUnit is used in model validation in neuroscience via NeuronUnit, which implements an interface to several simulators and model description languages, handles test calculations according to domain standards, and enables automated construction of tests based on data from several major public data repositories. ------- The aim of these sessions is to stimulate discussion of the development practices and tools used by different teams to improve the software we use while also improving our knowledge of these practices and tools. We also hope to encourage more users of these tools to contribute to their development to ensure their longevity. If you develop software for neuroscience, we would love to hear about your development pipeline. Please get in touch with the Software WG on our communication channels[2]. The WG is a community based group that is open to everyone at all levels of their careers (academic or otherwise). Please introduce yourself to the community on our channels to get involved. [1] https://ocns.github.io/SoftwareWG/ [2] https://ocns.github.io/SoftwareWG/pages/contact.html On behalf of the Software WG, -- Thanks, Regards, Ankur Sinha (He / Him / His) Research Fellow at the Silver Lab | http://silverlab.org/ Department of Neuroscience, Physiology, & Pharmacology University College London, London, UK Time zone: Europe/London

June 14, 2021

Postdoc on Alzheimer's Disease modeling (Inria, UCL, BCAM)

by desroche

Dear all, This is an invitation to join a multidisciplinary research on Alzheimer’s Disease (AD) for a 24-month postdoc. The project aims at combining mathematics and novel experiments to better understand the causes of the progressive symptoms of memory impairment in persons living with AD. Despite progress, AD research has hit a bottleneck and to date there are no effective treatments to prevent AD. It is now recognised that this is partially due to limitations in AD mice models that do not recapitulate human AD, to conflicting results and research focus on molecular mechanisms. We aim to overcome these limitations by exploring a gab in our understading of abnormal synaptic process (including neural-glia, inflammatory processes) and neural tissue dynamics that lead to AD. To this end, we will combine state-of-the-art experiments based on human brain tissue data, recent mice model that recapitulate human AD, mathematics and computations, which in tandem will address the multi-factorial nature of the disease for future successful drug development. The project is part of a long-standing collaboration between Mathieu Desroches (MathNeuro Project-Team, Inria Sophia Antipolis, France), Serafim Rodrigues (MCEN research group, Basque Center for Applied Mathematics, Bilbao, Spain) and Afia B Ali (School of Pharmacology, University College London, UK) on neurotransmission and its possible disruptions (see here <https://www.pnas.org/content/113/8/E1108.short>). The postdoc will strongly interact with these three researchers. Regarding profiles that we are seeking, familiarity of the candidate with mathematical modeling in Neuroscience. Experience with multiple-timescale systems is wishable but not mandatory. Ability and willingness to do programming, as well as analyse experimental data, will also be a strong point to select candidates. Please apply online via the following page: https://recrutement.inria.fr/public/classic/fr/offres/2021-03775 <https://recrutement.inria.fr/public/classic/fr/offres/2021-03775> For informal enquiries, please contact me: mathieu.desroches(a)inria.fr <mailto:mathieu.desroches@inria.fr> --------------------------------------------------------------------- Dr. Mathieu Desroches Research Scientist (Chargé de Recherche) MathNeuro Project Team Leader <https://team.inria.fr/mathneuro/> Inria Sophia Antipolis - Méditerranée, France http://www-sop.inria.fr/members/Mathieu.Desroches/ <http://www-sop.inria.fr/members/Mathieu.Desroches/>

June 14, 2021

CFP Workshop on Spatio-temporal Aspects of Embodied Predictive Processing @ ICDL2021

by Stefan Heinrich

Call For Contributions - Workshop on Spatio-temporal Aspects of Embodied Predictive Processing @ IEEE ICDL2021 Aims and Scope: Understanding human intelligence and building strong AI systems is a key challenge for our generation. A particularly puzzling aspect is that the human brain seems to cope very well with the highly variable and uncertain nature of perception and action, regarding both their signal characteristic and how they extend over time. Furthermore, it seems apparent that the information processing in the brain always involves previous bodily experiences and all our senses, thus is embodied and crossmodal. For explaining these characteristics, there are strong accounts that the brain is constantly predicting sensory input and feedback while minimising free energy in the prediction and is hierarchically abstracting the perception as well as hierarchically composing action. Furthermore, it is also hypothesised that mechanistic priors in the brain’s information processing induce a failure of hierarchical inference in the brain, accounting for atypical perception and action of psychiatric disorders. For example, some brains might have developed to focus too strongly on current sensory input while others might focus too strongly on memorised previous experience. A typically developed brain, in contrast, would show a fine balance compared to these two extreme priors. The big open mystery is: how is the brain developing this on a mechanistic level and thus how can this get learned within an AI system? Thus, to lift this mystery, we further need to bring together research from computational neuroscience, cognitive psychology, and artificial intelligence. With this workshop, we particularly want to wrap up different recent hypotheses, models, and experiments and discuss in-depth how to shape future imaging, behavioural, and developmental robotics studies as a complement to computational modelling and bio-inspired artificial intelligent algorithms. As a guiding theme, we aim to approach the following central questions: - How does the brain learn spatio-temporal stochasticity in perception and action? - What is the role of priors in learning spatio-temporal adaptive prediction? - How can developmental robotics help us to study spatio-temporal stochasticity as an analogy to infant learning? We examine these questions with the insight from invited key speakers from complementary fields as well as original contributions in breakout sessions in order to conclude the next steps within in a panel discussion. All information online: https://sites.google.com/view/stepp21/home Keynotes/Panellists: - Jun Tani, Cognitive Neurorobotics, Okinawa Institute of Science and Technology (OIST) - Marcel van Gerven, Artificial Cognitive Systems, Donders Institute, Radboud University - Takuya Isomura, Brain Intelligence Theory, RIKEN Center for Brain Science - Takamitsu Watanabe, Brain Network Dynamics, International Research Center for Neurointelligence (IRCN), The University of Tokyo Contributions: We invite submissions of extended abstracts on ongoing research that focuses on one or several of our guiding questions as contributions. In addition to the live presentation and discussion during the workshop, the contributions will be made public in workshop proceedings. - Extended abstract submission deadline: 11.07.2021 - Decision & Notification: 25.07.2021 - Camera-ready version due: 08.08.2021 - Workshop online: 22.08.2021 Submission website: https://sites.google.com/view/stepp21/ => Contribute Organisers: Stefan Heinrich, IRCN, The University of Tokyo, Japan Shingo Murata, Dept. Electronics and Electrical Engineering, Keio University, Japan Yukie Nagai, IRCN & Institute for AI and Beyond, The University of Tokyo, Japan Yuichi Yamashita, National Center of Neurology and Psychiatry, Japan -- ************************************************************* Dr. Stefan Heinrich Postdoctoral Project Researcher Cognitive Developmental Robotics Lab The University of Tokyo, Institutes for Advanced Studies, International Research Center for Neurointelligence Email: heinrich.stefan(a)ircn.jp https://developmental-robotics.jp/en/members/stefan_heinrich/ https://ircn.jp/en/ https://stefanheinrich.net/ *************************************************************

June 14, 2021

Multiple postdoctoral openings in reinforcement learning in multi-scale brain networks at Washington University in St. Louis

by Ching, ShiNung

Multiple postdoctoral openings in modeling reinforcement learning in multi-scale brain networks at Washington University in St. Louis Multiple postdoctoral openings are available for a new and large, multi-PI project to study the circuit underpinnings of reinforcement learning and decision-making. A key focus is on understanding multiple time-scales of information processing in neural circuits and involvement of neuron-astrocyte interactions. Experiment- and theory-forward positions are available. Applicants should have prior experience in neuroscience (computational or otherwise), modeling of large-scale systems, optimization, machine learning or related areas. Positions will be cross-appointed between Engineering and Neuroscience. Washington University in St. Louis is an independent, Research 1 university in St. Louis, MO, USA, with nearly 60 of the university's graduate and undergraduate programs ranked in the top 25 by US News & World Report. www.wustl.edu Interested applicants are invited to send a CV and brief description of interests to ShiNung Ching: shinung(a)wustl.edu

June 14, 2021

EITN Seminar: Paul Miller (Brandeis U, Waltham MA, USA) - Friday 18 June 16:00

by Zélie Tournoud

Dear colleagues, We are pleased to welcome *Paul Miller* from Brandeis University (Waltham MA, USA) for an online seminar this week. It will take place on Zoom on *Friday 18 June at 16:00* CEST (Paris time). *Title**: **Information processing in neural circuits with multiple quasi-stable attractor states* *Abstract: *Circuits containing clusters of neurons with strong within-cluster excitatory connections and random between-cluster connections can possess multiple activity states, with each state defined by the set of clusters with stably high versus low neural firing rates. Noise or adaptive processes can render the states quasi-stable such that in the presence of constant input, or following successive identical inputs, the network can progress through a sequence of different activity states. Here we study the properties of such models, including the role of finite size effects in producing multistability in networks with Gaussian random cross-coupling, and we assess the extent to which such state sequences might subserve information processing and behavior in a range of cognitive tasks. Join on the day on the event at: > Topic: EITN seminar: Paul Miller (Brandeis University) > Time: 18 June 2021 04:00 PM Paris (CEST) > Join Zoom meeting: > https://zoom.us/j/99754103845?pwd=UGlHSjgySTBXeUtvSW5SVUVMRnFiZz09 > <https://zoom.us/j/99754103845?pwd=UGlHSjgySTBXeUtvSW5SVUVMRnFiZz09> > Meeting ID: 997 5410 3845 > Password: 634477 We look forwards to see you at the seminar. Kind regards, The EITN Team

June 14, 2021

Adaptive Machines: Leveraging Neuroscience for Lifelong Learning Systems

by Andrea Soltoggio

Dear Connectionists, I’d like to bring your attention to the Research Topic “Adaptive Machines: Leveraging Neuroscience for Lifelong Learning Systems” to be co-hosted by Frontiers in Neuroscience, Robotics and AI, Big Data, Artificial Intelligence, Neurorobotics and Computational Neuroscience. The article submission deadline is the 30th of September. Link: https://www.frontiersin.org/research-topics/20053/adaptive-machines-leverag… Description: Artificial intelligence has always sought inspiration in the brain. Artificial neural networks (ANNs), in particular, were modeled after their biological counterparts (biological neural networks, BNNs). However, state-of-the-art deep networks are drastically different from BNNs. Their architectures (at both the single-neuron and macro levels), learning algorithms, and failure conditions share little in common with brain circuit dynamics. In particular, state-of-the-art deep learning is optimized for single, well-defined, static tasks. Deep networks struggle to learn multiple or evolving tasks over time (i.e., continual learning) or to adjust their processing based on environmental conditions. They are also less modular than BNNs, and thus less energy efficient, and utilize little-to-no feedback signals. We believe that deep networks suffer from these limitations because their modeling of brain dynamics is too superficial. Modeling more sophisticated neural mechanisms is therefore key for deep networks to achieve continual or lifelong learning and to cope with open-ended, dynamic environments. The goal of this Research Topic is to publish novel models and/or algorithms that expand the capabilities of deep learning (e.g., achieve better continual learning) by incorporating additional properties of BNNs. We are also interested in neuroscience research that elucidates mechanisms that can be leveraged by the AI community. Neuroscience has uncovered a wide range of learning and regulatory mechanisms, at scales ranging from single molecules to the entire nervous system, that help the brain learn, remember, and adapt. These include replay (systems-level consolidation), neuromodulation, neurogenesis, neuroevolution, attention, homeostatic plasticity, and synaptic consolidation. There exist some deep learning techniques motivated by these mechanisms (e.g., experience replay or attention networks), but they have rarely been used for lifelong learning. As such, our focus will be on approaches that leverage brain-like principles---ideally biologically well-grounded---to solve problems currently beyond the capabilities of the state of the art. We will also welcome neuroscience research that helps to elucidate the brain’s mechanisms, in the hope that they can be used by the next generation of machine learning models. The scope of this Research Topic covers: (1) biologically inspired techniques in deep learning (2) neuroscience research on how the brain facilitates learning and adapts to different contexts We seek to address continual or lifelong learning, transfer learning, and other ML scenarios that go beyond the traditional, train-then-test learning paradigm. Since machine learning is an experimental field, a paper must include experimental results to be accepted; however, we encourage authors to include theoretical analyses of their methods. We aim to collect the following types of manuscripts: Original Research and Brief Research Reports. Since the goal of this Research Topic is to publish highly novel, experimentally grounded work, we are not interested in Reviews (Systematic, Policy and Practice, etc.), General Commentaries, or Opinions. In addition, since the editorial board does not include medical experts, we cannot accept Clinical Trials or Study Protocols. The Research Topic will be edited by Rolando Jose Estrada, Georgia State University, USA Andrea Soltoggio, Loughborough University, UK Praveen K. Pilly, HRL Laboratories, USA Vincenzo Lomonaco, University of Pisa, Italy, Davide Maltoni, University of Bologna, Italy. Please consider submitting your original research to this Research Topic. Do not hesitate to get in touch if you wish to discuss your contribution. Best wishes, Andrea -- Dr. Andrea Soltoggio (he/him or they/them) Senior Lecturer (Associate Professor) in Artificial Intelligence Department of Computer Science, School of Science & Intelligent Automation Centre https://www.intelligent-automation.org.uk/about-us/centre-staff & Centre for Information Management https://www.lboro.ac.uk/departments/sbe/cim/ Haslegrave Building, N.2.03 Loughborough University LE11 3TU, UK Email: a.soltoggio(a)lboro.ac.uk<mailto:a.soltoggio@lboro.ac.uk> Web: http://www.lboro.ac.uk/departments/compsci/staff/dr-andrea-soltoggio.html

June 14, 2021

be a mentor for Neuromatch Academy!

by Marius Pachitariu

Participate as MENTOR in Neuromatch Academy Neuromatch Academy returns this year with twice as many students and TAs participating in twice as many courses. If you are faculty or postdoc with some experience, please sign up to be a mentor for the Computational Neuroscience (syllabus <https://github.com/NeuromatchAcademy/course-content>) and/or the Deep Learning course (syllabus <https://github.com/NeuromatchAcademy/course-content-dl>). NMA is unique in reaching enthusiastic students from all over the world, most of whom cannot afford a traditional summer school. NMA 2020 reached students from ~70(!) countries. This year we have students from >100 !!! Mentors provide guidance and feedback on 3-week long projects that students complete in groups of 4-6. You will be algorithmically matched with students that have similar research interests to you.The mentorship happens in 4-5 sessions of 30-60 minutes each, leading to a time commitment of up to 5 hours per group. Mentors can take on 1-4 groups. Beginner groups are encouraged to use project templates, such as the ones here (link <https://docs.google.com/presentation/d/1v7fuzi9Y9RV12CysMBi5rxZKhjnWD5Js29U…>). Please consider contributing a project template, by simply adding a slide to the public google doc. Mentoring is relatively easy because you don’t need to prepare anything. Nonetheless, your feedback is extremely valuable to the students, who are taking their first steps in neuroscience / deep learning. We also encourage you to provide career advice, and optionally set up meetings with TAs after the summer school. Our collective effort to teach and mentor these young scientists and engineers pays off in the long-term: their positive experience at NMA inspires them to consider careers in our research fields. The application for mentors can be found below. Course starts July 5th, so please apply soon! https://portal.neuromatchacademy.org/

June 11, 2021

Senior Postdoc ("Akademischer Rat") in the Reichenbach lab, Friedrich-Alexander-University (FAU) Erlangen-Nuremberg, Germany

by Reichenbach, Tobias

The newly founded Chair for Sensory Neuroengineering at the new Department Artificial Intelligence in Biomedical Engineering is looking for a Senior Postdoc ("Akademischer Rat"). In close collaboration with clinical and industrial partners, the Chair researches on the neural foundations of hearing, as well as on new technology for better treatments of hearing impairment. Reseach topics are, for instance, the development and application of AI methods to analyse neurological data on human speech processing as well as the development of AI methods to construct audiovisual speech signals for better speech comprehension. The Senior Postdoc should shape this research field with their own innovative projects. The appointment will initially be for three years, and can be made permanent upon excellent performance. To apply, please send a letter detailing your motivation, a CV, copies of your degrees and details of three referees. Please combine all documents into a single pdf and send them per email with subject “Application Senior Postdoc” to Prof. Dr. Tobias Reichenbach (tobias.j.reichenbach(at)fau.de<http://fau.de>). FAU is the most innovative university in Germany and ranks second among the most innovative European universities. It is located in one of the most attractive regions in Germany. Erlangen is a bicycle-friendly city with 100,000 inhabitants, former home of the Huguenots as well as a key location for Siemens. It is close to Nuremberg, a city with a population of half a million and a metropolitan flair. The mixture of academia, innovative companies, art and culture makes the region the perfect place for creative minds, keen athletes and nature lovers. More information is available here: https://www.jobs.fau.de/jobs/senior-postdoc-in-computational-neurotechnolog… ------------------------------------------- Prof. Dr. Tobias Reichenbach Department Artificial Intelligence in Biomedical Engineering (AIBE) Friedrich-Alexander-University (FAU) Erlangen-Nuremberg Henkestrasse 91 91056 Erlangen https://reichenbach.bg-research.cc.ic.ac.uk/index.html

June 11, 2021

Information theory workshop at CNS*2021 -- call for contributed talks

by Joseph Lizier

Dear all, We are pleased to announce that the Workshop on Methods of Information Theory in Computational Neuroscience<https://abzinger.github.io/202107-CNS2021-ITW.html> will be held once again at the CNS*2021<http://www.cnsorg.org/cns-2021> conference, online. The workshop will be held during sessions over two days of the main conference, July 6 and 7 (time TBA). Our confirmed speakers so far include the following (schedule available soon): * Tatyana Sharpee, Salk Institute * Raul Vicente, University of Tartu * Viola Priesemann, Max Planck Institute for Dynamics and Self-Organization * Fernando Rosas, Imperial College London * Fleur Zeldenrust, Radboud University * Aaron Gutcknecht, University of Goettingen * Lucas Rudelt, Max Planck Institute for Dynamics and Self-Organization * ... more TBA! Also, we would like to call for contributions of talks. If you are interested in contributing a talk, please send a title and abstract to Abdullah Makkeh (abdullah.alimakkeh(a)uni-goettingen.de<mailto:abdullah.alimakkeh@uni-goettingen.de>) at your earliest convenience (earlier submissions will be prioritised). Please pay attention to the registration<https://www.cnsorg.org/cns-2021-registration-fees> for CNS*2021 (early bird deadline and fees), and see our website<https://abzinger.github.io/202107-CNS2021-ITW.html> for more details. We hope you will join us there! Organising Committee: Michael Wibral (co-chair) Abdullah Makkeh (co-chair) Joseph Lizier

June 10, 2021

TEX2021 - Bringing together Predictive Processes and Statistical Learning

by Yamil Vidal

You are all invited to: TEX2021 - Bringing together Predictive Processes and Statistical Learning A free online symposium organized by Yamil Vidal and Davide Crepaldi. Description: While the research communities studying Predictive Processes and Statistical Learning sprout from different traditions, they share a core interest in how our brains pick up on patterns in the environment. In this two-days symposium we will bring these communities closer by sharing talks by some of the leading researchers of these fields. When: 1st and 2nd of July Speakers: - Tristan Bekinschtein (Consciousness and Cognition Lab, Department of Psychology, University of Cambridge) - Lucia Melloni (Department of Neuroscience, Max Planck Institute for Empirical Aesthetics. Department of neurology, NYU Grossman School of Medicine) - Padraic Monaghan (Department of Psychology, Lancaster University) - Maria Chait (Ear Institute, University College London) - Floris de Lange (Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen) - Caspar Schwiedrzik (Neural Circuits and Cognition Lab, European Neuroscience Institute Goettingen. Perception and Plasticity Group, German Primate Center - Leibniz Institute for Primate Research) - Lori Holt (Department of Psychology, Carnegie Mellon University) - Nicola Molinaro (BCBL, Basque Center on Cognition, Brain and Language) Schedule and more info at the registration form: https://sissa-it.zoom.us/webinar/register/WN_nBOv5NWZQ36HSH7nyffazg And here is the announcement that we made through Twitter: https://twitter.com/CrepaldiLab/status/1400856625896902656 We would be very happy to count you among the attendees. Please spread the word. Best regards, Yamil and Davide Dr. Yamil Vidal Cognitive Neuroscience Sector Scuola Internazionale Superiore di Studi Avanzati - SISSA via Bonomea, 265 34136 Trieste, ITALY

June 8, 2021

EITN Fall School in Computational Neuroscience : Applications open!

by Zélie Tournoud

Dear colleagues, We are happy to let you know that applications for the *EITN Fall School in Computational Neuroscience* are currently open, until the 2nd of July 2021. This *10-days intensive course in computational neuroscience* will take place in *P**aris, France from 15 to 24 September 2021*. This is a yearly event, although previous occurrences took place in the spring. We choose to keep it relatively small-scale in order to facilitate interactions between participants and teaching staff, there are about 20 seats available. There will, therefore, be a selection made after applications are closed. We encourage you to share the word widely with your collaborators and networks so that everyone who might be interested can get the chance to apply. Applications need to be addressed by email with a *resume (CV)* and a *cover letter* at *eitn(a)neuro-psi.fr*. Please make sure to use an explicit email title such as "Application to EITN Fall School". *Why join?* * 10 days of intensive training in computational neuroscience provided by researchers from all over Europe * Small scale event: get to connect! * A complete program: o Cellular models, and models of brain signals Circuit models and networks o Mean-field models o Whole-brain models * Hands-on learning schedule based on: o Morning classes and tutorials o Group projects in the afternoon o Free time in the evenings to visit Paris *Selection criteria:* 1. Work or study in neuroscience or related 2. Experience in python programming would help 3. Priority to PhD students, although Master students and post-docs are welcome to apply *Practical information:* * Subscription fee 400€. * If selected, you will need to bring your own laptop with a few pre-installed programs. * The tuition fee includes lunch, coffee, access to conference facilities and internet connection during the 10 days of the course. You can also consult these information online <https://www.eitn.org/index.php/calendar-event/eventdetail/737/-/eitn-fall-s…> or by downloading the PDF flyer: https://www.eitn.org/images/events/EITN-FallSchool-2021_Flyer.pdf Thank you very much for applying or sharing with your relevant contacts. Kind regards, The EITN Team

June 8, 2021

Postdoc Position at Cardiff University Brain Research Imaging Centre

by Jiaxiang ZHANG

[Apologize for cross-posting] Dear all, We are seeking an enthusiastic early-career scientist for a research associate position at Cardiff University Brain Research Imaging Centre (CUBRIC). The successful candidate will join a collaborative and multidisciplinary research team to work on the neurocognitive mechanisms of human decision-making, integrating multimodal brain imaging (MEG, functional MRI and diffusion MRI) and computational modelling. The post holder will develop computational models that describe data from behavioural and brain imaging experiments, in particular to take a lead role in integrating large-scale computational simulations with imaging results from multiple modalities. The research aims to understand the computational and neurocognitive processes of human decision-making, and how decision deficits may arise due to brain injury or neurodegeneration. The research associate will also be expected to collaborate with other researchers in the team on imaging data analyses and data curation. You will have access to imaging facilities at CUBRIC and several multimodal imaging datasets. Cardiff University Brain Research Imaging Centre (CUBRIC) houses a unique combination of state-of-the-art facilities and world-leading expertise, with 4 human MRI systems (2 x Siemens Prisma, 1 x Siemens Connectom, 1 x Siemens 7T), MEG, EEG, TMS, tDCS, clinical research units and testing labs. Further details of CUBRIC can be found on our webpage (http://sites.cardiff.ac.uk/cubric) This is a full-time, fixed-term position until February 2023, available to start now. Remote working is possible if COVID-19 restrictions are in place. For informal enquiries, please contact Dr Jiaxiang Zhang ( zhangj73(a)cardiff.ac.uk) A formal application needs to be submitted via the link online ( https://www.jobs.ac.uk/job/CEQ831/research-associate) *Application deadline: June 13th, 2021.* *Essential Criteria* 1. A PhD degree in a relevant discipline (e.g., mathematics, computer science, engineering, physics or psychology) 2. Research experience or a strong interest in human decision-making 3. Strong experience in computational models of cognitive processes and/or macroscopic neural systems, including the use of neural mass models or neural field models 4. Experience of analysing brain imaging data in one or more modalities (e.g., diffusion MRI, BOLD functional MRI, MEG/EEG), using common software packages 5. Strong programming skills (e.g., Matlab, Python, C/C++ or R) 6. Experience with Unix/Linux environment for data analysis, including bash scripting and parallel computing on high-performance clusters 7. Proven ability to publish high-quality research in peer-reviewed academic journals 8. Knowledge of software development cycles and best programming practices, including coding standards, version control administration, debug and code sharing, with strong commitments to open science practices 9. Excellent written and verbal communication skills, evidenced by publications and/or conference presentations 10. Strong interpersonal skills, including proven ability to work cooperatively within a team while also being able to take the initiative *Desirable Criteria * 1. Experience of brain functional and structural connectivity analyses 2. Experience in statistical methods (e.g., GLM, mixed models, multivariate statistics, hierarchical models and Bayesian statistics) and/or machine learning methods 3. Experience in deep learning models of imaging processing --- Dr. Jiaxiang Zhang Cardiff University Brain Research Imaging Centre (CUBRIC) School of Psychology Cardiff University Maindy Road, Cardiff, CF24 4HQ https://ccbrain.org

June 8, 2021

[jobs] One-year Postdoc on “Sparse coding for bio-inspired vision models” at CentraleSupèlec, Gif-sur-Yvette, France

by PERRINET Laurent

Dear Colleagues, Applications are invited for a one-year Postdoc position, starting in September 2021 or as soon as possible thereafter, to work under the joint supervision of Dario Prandi<https://dprn.github.io/> (CNRS, Université Paris-Saclay), Luca Calatroni<https://sites.google.com/view/lucacalatroni/home> (CNRS, Université Côte d'Azur), and Laurent Perrinet<https://laurentperrinet.github.io/> (CNRS, Institut de Neurosciences de la Timone). The position is financed by the ANR Rubin-Vase (https://anr.fr/Projet-ANR-20-CE48-0003) and the research focus will be on linking the bio-inspired models studied by D. Prandi and L. Calatroni (see e.g. [1]) to the sparsity promoting neural architectures investigated by L. Perrinet (see, e.g., [2]). The successful applicant will have a strong applied mathematical background, ideally in the fields of sparse optimization and/or dynamical systems, and a good familiarity with neural architectures and the associated implementation issues. Strong skills in coding and programming using standard optimization/deep learning languages (e.g., Python, PyTorch, Julia, MATLAB…) are required. The gross salary is of 50k-59k euros (depending on the seniority of the applicant), and the position comes with no teaching duties. Although the position is officially based at CentraleSupèlec (Université Paris-Saclay), the successful applicant is expected to spend fairly long periods at the INT in Marseille and I3S in Sophia-Antipolis, to work with the co-supervisors. Candidates are asked to write directly to Dario Prandi (dario.prandi(a)centralesupelec.fr<mailto:dario.prandi@centralesupelec.fr>), attaching a CV, a short description of current research interests, and names and contact addresses of at least two reference contacts (no formal letters of recommendation are required). Applications are reviewed continuously, until positions are filled, with a first evaluation round starting on the 14th of June, 2021. Thanks for distributing this announcement to potential candidates! References [1] Bertalmío, M., Calatroni, L., Franceschi, V. et al. Cortical-Inspired Wilson–Cowan-Type Equations for Orientation-Dependent Contrast Perception Modelling. J Math Imaging Vis 63, 263–281 (2021). https://arxiv.org/abs/1910.06808 [2] Boutin V, Franciosini A, Chavane F, Ruffier F, Perrinet L (2021) Sparse deep predictive coding captures contour integration capabilities of the early visual system.<https://laurentperrinet.github.io/publication/boutin-franciosini-chavane-ru…> PLoS Comput Biol 17(1): e1008629. https://doi.org/10.1371/journal.pcbi.1008629 -- Laurent Perrinet - INT (UMR 7289)/CNRS https://laurentperrinet.github.io/

June 7, 2021

[news] Call for Participation - The Chef's Hat Cup @ IJCAI2021

by Pablo Vinicius Alves De Barros

Deadline Extended! - The Chef's Hat Cup @ IJCAI2021 I. Aim and Scope Most of the current Reinforcement-Learning solutions, although having real-world-inspired scenarios, focus on a direct space-action-reward mapping between the agent's actions and the environment’s state. That translates to agents that can adapt to dynamic scenarios, but, when applied to competitive and/or cooperative cases, fail to assess and deal with the impact of their opponents. In most cases, when these agents choose an action, they do not take into consideration how other agents can affect the state of the scenario. In competitive scenarios, the agents have to learn decisions that a) maximize their chances of winning the game, and b) minimize their adversaries' goals, while in cooperative scenarios b) is inverted. Besides dealing with complex scenarios, such solutions would have to deal with the dynamics between the agents themselves. In this regard, social reinforcement learning is still behind the mainstream applications and demonstrations of the last years. We introduce here the Chef's Hat Cup, a competition that aims at the development of the most challenging artificial players! The competition is separated into two tracks: a competitive and a cooperative scenario. In the first track, the participants will use the already available simulation environment to develop the most effective agents to play the Chef’s Hat card game and be the winner. In the second track, they will have to develop an agent that can increase the chances of a dummy agent winning the game. More information: https://www.whisperproject.eu/chefshat#competition II. Deadlines Starting Date: April 20, 2021 Final submission: July 15, 2021 Winners Announcement: July 29, 2021 III. Team Registration Teams of up to five persons are invited to participate in the competition. You can register by sending the following information: Team name Participant Names and affiliation to this email: pablo.alvesdebarros(a)iit.it IV. Prizes The prizes will be sponsored by the Contact Unit at the Italian Institute of Technology, and will be organized as follows: Competitive Track - Winner – 300 EUR - Second – 150 EUR - Third – 50 EUR Cooperative Track - Winner – 300 EUR - Second – 150 EUR - Third – 50 EUR V. Organizers Pablo Barros, Italian Institute of Technology (IIT), Italy Leandro Honorato, University of Pernambuco, Brazil Marco Matarese, Italian Institute of Technology (IIT), Italy Alessandra Sciutti, Italian Institute of Technology (IIT), Italy ---------------------------------------- Dr. Pablo Barros Postdoctoral Researcher - CONTACT Unit Istituto Italiano di Tecnologia – Center for Human Technologies Via Enrico Melen 83, Building B 16152 Genova, Italy email: pablo.alvesdebarros(a)iit.it website: https://www.pablobarros.net<http://www.pablobarros.net><https://www.p> twitter: @PBarros_br

June 3, 2021

PostDoc in Machine Hearing

by Sarah Verhulst

Dear list, We have a 3-yr post-doc vacancy in my Hearing Technology lab in Ghent, Belgium. We do research on the interface between computational neuroscience and audio signal processing to develop human-inspired machine-hearing applications. More info on the position and application info can be found here (closing date 5th of July): https://tinyurl.com/4h9m497b Feel welcome to distribute, With best regards, Sarah —— Sarah Verhulst Associate Professor Hearing Technology @ WAVES Department of Information Technology Technologiepark 126, 9052 Zwijnaarde T +32 9 331 48 44 T admin +32 09 264 33 21 www.waves.intec.ugent.be/hearing-technology<http://www.waves.intec.ugent.be/hearing-technology> www.gasp.ugent.be<http://www.gasp.ugent.be> @HearingTechLab @GentAuditorySci GitHub: HearingTechnology

June 2, 2021

Research fellowships at the Univ. of Birmingham/UK

by Michael Biehl

*Two research fellowships at the * *Centre for Systems Modelling and Quantitative Biomedicine, College of Medical and Dental SciencesUniversity of Birmingham /UK **Full time, fixed term for 3.5 years* *Closing Date *27th June 2021. Online interviews will take place on 20th July 2021 For further details and information about how to apply visit *https://www.jobs.ac.uk/job/CGC736/research-fellows <https://www.jobs.ac.uk/job/CGC736/research-fellows> * (do not reply to this message, do not contact M. Biehl directly) The successful candidates will be educated to PhD level and have experience in one or more of the following areas: Position 1: mathematical models; complex networks, (nonautonomous) dynamical systems, bifurcation theory or numerical continuation. Having relevant experience in biology or medicine will be advantageous. Position 2: computational methods; machine learning, neural networks, data visualisation and analytics, artificial intelligence, Bayesian inference, or algorithm development. Having relevant aligned experience in biology, medicine, or wearable technologies will be advantageous. -- ---------------------------------------------------------- Prof. Dr. Michael Biehl Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence P.O. Box 407, 9700 AK Groningen The Netherlands Tel. +31 50 363 3997 https://www.cs.rug.nl/~biehl m.biehl(a)rug.nl

June 2, 2021

EITN workshop on "Computational Neuroscience in EBRAINS", 10 june 2021

by Zélie Tournoud

Dear colleagues, We are happy to invite you to attend to the EITN (European Institute for Theorethical Neuroscience) workshop on "Computational Neuroscience in EBRAINS" taking place online on *June 10th, 2021, starting from 13:00 CEST*. Attendance is free and open to everyone upon registration, feel free to share the event to your collaborator who might be interested in the topic. To automatically receive your access link by email before the event, *register online* on this page: Register to the Zoom meeting <https://zoom.us/meeting/register/tJ0vd-CoqzkoHNTvv9UhOQ0Qbnex9MikG2F1>. The detailled program is available on this page <https://www.eitn.org/index.php/calendar-event/eventdetail/744/-/workshop-on…> that will be kept updated. * *Description of the workshop:* Organized by Andrew Davison (CNRS), Viktor Jirsa (AMU) and Alain Destexhe (CNRS). EBRAINS is a open-access platform built by the Human Brain Project and which provides tools for various applications in neuroscience. The aim of this workshop is to introduce some of the EBRAINS tools that are of particular interest for applications in computational neuroscience. These tools provide help to simulate models, from the cellular to the whole-brain level. Each tool will be introduced, then a "live demo" will be provided to demonstrate its use. As a companion to this workshop, we plan to provide recordings of each session on a YouTube channel, as well as scripts that were used in for the live demos. * *List of speakers:* Egidio d'Angelo (Pavia) Claudia Casellato (Pavia) Florent Bonnier (CNRS) Petrut Bogdan (U Manchester) Andrew Davison (CNRS) Alain Destexhe (CNRS) Timo Dickscheid (Jülich) Jan Fousek (AMU) Alice Geminiani (Pavia) Jennifer Goldman (CNRS) Brent Huisman (Jülich) Viktor Jirsa (AMU) Carmen Lupascu (CNR) Armando Romani (BBP/EPFL) Michael Schirner (Charité) Sam Yates (CSCS) We look forwards on seeing you on the 10th of June! Thank you and have a great day, /The EITN Team/

June 2, 2021

[Journals] 2nd CFP: Special Issue on "Theory of Mind in Humans and in Machines"

by Bogdan Ionut Cirstea

Dear colleagues, Apologies for cross-posting. Together with Prof. Fabio Cuzzolin (Oxford Brookes University, UK), Prof. Barbara Jacquelyn Sahakian (University of Cambridge, UK) and Dr. Christelle Langley (University of Cambridge, UK), we are organizing a special issue (Frontiers Research Topic) on the emerging topic of "Theory of Mind in Humans and in Machines" under the "Frontiers in Artificial Intelligence" and "Frontiers in Computational Neuroscience" journals. We would like to invite you to contribute articles to the Research Topic. For more information on this special issue, please visit the website at https://www.frontiersin.org/research-topics/16802/theory-of-mind-in-humans-… Papers may be submitted until the 30th of July 2021 (manuscript submission extended deadline). Submitted papers should not be under consideration for publication elsewhere. We also encourage authors to send a short abstract (via the platform) to ensure compliance with the topic and scope of the Frontiers journals as early as possible. Topics of interest include, but are not limited to, theoretical proposals, computational experiments, and case studies of: • Computational Theory of Mind • Learning from Demonstrations • Cognitive Models for Learning from Demonstration and Planning • Neuroscience-inspired models of Theory of Mind • Human-Robot Interaction • Cooperative Inverse Reinforcement Learning (assistance games) • Learning from Preferences If you intend to send an article to the special issue, please do let us know by hitting the "Participate" button in the web interface. If you have any questions or need any kind of assistance from us, please do not hesitate to let us know. On behalf of the topic editors, -- Bogdan-Ionut Cirstea Topic Editors: Prof. Fabio Cuzzolin, Oxford Brookes University, UK < fabio.cuzzolin(a)brookes.ac.uk> Prof. Barbara Jacquelyn Sahakian, University of Cambridge, UK < bjs1001(a)cam.ac.uk> Dr. Bogdan-Ionut Cirstea, Oxford Brookes University, UK < bcirstea(a)brookes.ac.uk> Dr. Christelle Langley, University of Cambridge, UK <cl798(a)medschl.cam.ac.uk >

June 1, 2021

Evolving Neural Networks workshop

by manuel molano

Dear all, We are glad to announce the 1st Evolving Neural Networks Workshop (June 17, 2021). The goal of the workshop is to bring together experts from Systems and Computational Neuroscience, Machine Learning and the Evo-Devo field to discuss if and how knowing the evolutionary history of neural circuits can help us understand the way the brain works, as well as the relative importance of learned VS innate neural mechanisms. We have a great list of speakers from the fields of Systems and Computational Neuroscience, Machine Learning and Evolutionary Developmental (Evo-Devo) biology. If you are interested REGISTER HERE<https://docs.google.com/forms/d/e/1FAIpQLSfO69TaE5Tk4eUysh4eCnJ7tlJYRTCcE_m…>, it is free! For more information visit our webpage<https://sites.google.com/view/evolving-nns/home> or contact evolvingneuralnetworks(a)gmail.com<mailto:evolvingneuralnetworks@gmail.com> We hope to see you soon! SCHEDULE (Eastern Time) 8-8.10 Intro 8.10-8.35 Pamela Lyon What basal cognition can do for you (neuroscientists) 8.35-9.00 Luis Puelles Brain evolution crosses an ontogenetic 'ant's country' that escapes analysis 9.00-9.25 Paul Cisek The evolution of the human brain, long before humans 9.25-9.50 Robert Yang Evolving the Olfactory System with Machine Learning 09.50-10.20 Discussion 10.20-10.40 Break 10.40-10.50 Intro 10.50-11.15 Linda Wilbrecht Considering contextual factors at acute and evolutionary timescales in the study of learning and decision making 11.15-11.40 Ida Momennejad TBA 11.40-12.05 Dayu Lin Neural mechanisms of aggression 12.05-12.30 Tony Zador Learning through the Genomic Bottleneck 12.30-13.00 Discussion

June 1, 2021

Job: Engineer in MEG-EEG signal processing and open-source software development

by Franco Pestilli

The Paris Brain Institute is recruiting a motivated individual to join the *MEEGbrainlife* project, introducing MEG and EEG data processing pipelines on the open-access brainlife.io cloud computing platform. This is a 18-24 months contract funded jointly by the French ANR and the American NIH under the international program call « Collaborative Research in Computational Neuroscience » (CR‐CNS). The successful candidate will be responsible for planning, developing, and maintaining *MEG‐EEG data processing Apps into brainlife.io <http://brainlife.io/>* and *contribute to innovative research projects* on the platform. In close collaboration with the expert members of the MEEGbrainlife consortium, he/she will define data specifications, code architecture, data and code output quality check procedures to set the groundwork for the rapid development of a full data processing pipeline for MEG‐EEG data into brainlife.io. *More information and contact* for the application available here: https://owncloud.icm-institute.org/index.php/s/MBrIpIFcH3F9lV5 *FRANCO PESTILLI*, *Ph.D.* | *Associate Professor* Department of Psychology <https://liberalarts.utexas.edu/psychology/index.php> | College of Liberal Arts <https://liberalarts.utexas.edu/> | The University of Texas at Austin <https://www.utexas.edu/> he/him | pestilli(a)utexas.edu | web <https://liberalarts.utexas.edu/psychology/faculty/fp4834> | GitHub <https://github.com/francopestilli> | brainlife.io CONFIDENTIALITY NOTICE: This e-mail message, including any attachments, is for the sole use of the intended recipient(s) and may contain confidential and privileged information. Any unauthorized review, use, disclosure or distribution is prohibited. If you are not the intended recipient, please contact the sender by reply e-mail and destroy all copies of the original message.

May 31, 2021

pre- and post-doc neuromorphic fellowships at the Sevilla Microelectronics Institute

by bernabe

Dear all, The Neuromorphic group at IMSE (Sevilla Microelectronics Institute, Spain) wellcomes candidates to apply for pre- and post-docs fellowships on neuromorphic engineering. Please visit our web-site at www.imse-cnm.csic.es/neuromorphs to find out about our running projects, activities, and publications. More details on the fellowships (duration/salary/contact) is given at: http://www2.imse-cnm.csic.es/neuromorphs/index.php/News-and-position-openin… cheers, -- Bernabe Linares-Barranco, Director Instituto de Microelectrónica de Sevilla (IMSE-CNM), CSIC, Sevilla, Spain

May 31, 2021

Job opening: Research Scientist - Computational Neuroscience and Machine Learning/AI

by Michiel Remme

Dear Colleagues, Our startup INAIT, based in Lausanne, Switzerland, is seeking a permanent and full-time Research Scientist at the intersection of computational neuroscience and machine learning/AI. Are you interested in developing new machine learning/AI methods based on neuroscientific insights, and apply them to business problems? Then come join us! Major duties and responsibilities: * Contribute to the research and development of the next generation of neuroscience-inspired machine learning/AI methods * Contribute to the development of computational models of detailed and/or simplified neuronal networks * Work closely with a team of computational neuroscientists, machine learning engineers and software developers towards business solutions Essential skills and experience required: * PhD in computational neuroscience or related field (or equal experience: Masters and 4+ years of research experience) * Research involving learning and/or dynamics in rate-based and/or spiking neural networks * Experience in machine learning using frameworks such as scikit-learn, PyTorch or TensorFlow * Successful publication record * Experience in Python code development * Experience using UNIX/Linux operating systems * Excellent team player * Fluent spoken and written English Preferred: * Experience with a computational neuroscience simulation framework such as NEST, NEURON or Brian * Experience in scientific computing on shared and/or distributed parallel machines * Experience in the software development life-cycle including unit testing, continuous integration, version control, debugging and documentation * Successful track record in designing, developing and maintaining software projects Where: Lausanne, Switzerland Start date: As soon as possible Activity rate: 100% Duration of contract: CDI Competitive salary & benefits Cutting edge research, Positive work environment, Hybrid work model (combination of WFH and work from the office) For applications visit apply.workable.com/inait<http://apply.workable.com/inait/> INAIT SA https://www.inait.ai<https://www.inait.ai/> Avenue du Tribunal Fédéral 34 1005 Lausanne Switzerland

May 30, 2021

Call for papers for MLCN2021 workshop at MICCAI 2021

by MLCN Workshop

*Please find below the call for papers for the International Workshop of Machine Learning in Clinical Neuroimaging (MLCN) which is held virtually on the 27th of September 2021 at MICCAI 2021 in Strasbourg, France. We welcome contributions to novel machine learning methods and their applications to clinical neuroimaging data.* The submission deadline is *25 June 2021*, and all MLCN accepted papers will be eligible for the best paper award of 500 USD. For more information, please visit https://mlcnws.com/call-for-papers/*.* Best wishes, The MLCN 2021 steering and organizing committees Christos Davatzikos, Andre Marquand, Jonas Richiardi, Emma Robinson Ahmed Abdulkadir, Mohamad Habes, Seyed Mostafa Kia, Vinod Kumar, Jane Maryam Rondina, Chantal Tax, Thomas Wolfers ================================================================================================ Call for Papers The International Workshop of Machine Learning in Clinical Neuroimaging ( https://mlcnws.com/) a satellite event of MICCAI (https://miccai202 <https://miccai2021.org/>1 <https://miccai2020.org/>.org <https://miccai2021.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. In the machine learning track, we seek novel contributions that address current methodological gaps in analyzing high-dimensional, longitudinal, and heterogeneous clinical neuroscientific data using stable, scalable, and interpretable machine learning models. Topics of interest include but are not limited to: - Big data - 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*,* the applications of existing machine learning algorithms are evaluated to move towards precision medicine for complex brain disorders. The discovery of biological markers in medicine is an important challenge across different fields and various experimental procedures and designs are used to detect biological signatures that can be utilized for improvement in diagnostic, treatment, or for other beneficial ends. However, for most complex brain disorders, we do not have reliable biomarkers today. The application of advanced machine learning methods may help to reach this goal. Therefore, we invite the community to submit conference contributions on machine learning approaches with the goal to improve our understanding of complex brain disorders, moving the field closer towards precision medicine. 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 - The presentation of clinical neuroimaging databases to stimulate developments in machine learning - … ------------------------------ 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://miccai2021.org/en/PAPER-SUBMISSION-GUIDELINES.html for detailed author guidelines) using the CMT system at https://cmt3.research.microsoft.com/MLCN2021. 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 2021 conference. ------------------------------ MLCN Special Issue at the MELBA journal: This year, we will invite the top accepted papers to submit an extended version of their contribution to the MLCN special issue at the Journal of Machine Learning for Biomedical Imaging (MELBA) <https://www.melba-journal.org/about>. The invited papers will go through an independent review process by the journal. ------------------------------ Best Paper Award: 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, novelty, and clarity of contributions. The winner will be announced at the end of the workshop and will receive a 500 USD honorarium. ------------------------------ Important Dates: - Paper submission deadline: June 25, 2021, 11:59 PM Pacific Time - Notification of Acceptance: July 16, 2021 - Camera-ready Submission: July 30, 2021 - Workshop Date: September 27, 2021

May 30, 2021

Postdoc in Cognitive and Systems Neuroscience

by Theresa Desrochers

Applications for Postdoctoral Research Associate are being sought for a unique opportunity to study sequential control in the laboratory of Dr. Theresa Desrochers in the Department of Neuroscience at Brown University. Successful candidates will have interest in human behavior, fMRI, TMS, computational psychiatry, and OCD. The Desrochers Lab provides a unique cross-level and cross-species training environment situated within a highly collaborative research environment fostered by Brown University and the Carney Institute for Brain Science. Prior experience in human neuroimaging and strong quantitative skills are preferred. All interested candidates regardless of level of experience with these methods are encouraged to apply. Applicants must hold a Ph.D. in Neuroscience, Cognitive Science, Psychology, or a related discipline or must show evidence that the Ph.D. will be completed before the start of the position. Brown University is an EEO/AA employer and encourages applications from minorities and women. Review of applications will begin immediately and will continue until the position is filled or the search is closed. For more information, please contact theresa_desrochers(a)brown.edu. Please apply via Interfolio at https://apply.interfolio.com/87606 <https://apply.interfolio.com/87606>. ~Theresa — Theresa M. Desrochers, Ph.D. Rosenberg Assistant Professor of Brain Science Departments of Neuroscience; Psychiatry and Human Behavior (courtesy) Carney Institute for Brain Science, Brown University http://neuroscience.brown.edu/desrochers

May 28, 2021

PhD positions at Vrije Universiteit Amsterdam

by Avitabile, D.

Dear colleagues, The Department of Mathematics at the Vrije Universiteit Amsterdam invites applications for 2 PhD positions, in computational and analytical methods form neuroscience applications. One project concerns uncertainty quantification methods for spatially-extended models in neuroscience. The ideal candidate for this project has a passion for numerical methods (numerical analysis, scientific computing) and is keen to make an impact in a novel application for the neurosciences. Previous knowledge of neuroscience concepts is not a prerequisite, but a firm interest in learning about neuroscience models and applications is essential. The project will be co-supervised by Svetlana Dubinkina (VU), Gabriel Lord (Radboud University), and myself. The second PhD project is about multiple timescale analysis for neural fields, and it aims to develop slow-fast dynamical system methods for spatially-extended systems. The ideal candidate has a passion for dynamical system theory and is interested in applying bifurcation analysis to PDEs and integral equations. Previous knowledge of neuroscience concepts is not a prerequisite. The project will be cosujervised by Jan Bouwe van den Berg (VU) and myself. It will also involve a network of scientific collaborators: Mathieu Desroches (INRIA, Sophia Antipolis, France), Grégory Faye (University Paul Sabatier, Toulouse, France) and Martin Wechselberger (University of Sydney, Australia). More information can be found at the link below https://werkenbij.vu.nl/ad/phd-positions-in-mathematical-neuroscience/ngla4… The deadline for applications is the 13th of June 2021. Informal enquiries can be sent to d.avitabile(a)vu.nl Daniele Avitabile Associate Professor in Applied Mathematics Vrije Universiteit Amsterdam www.danieleavitabile.com

May 28, 2021