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MSc Internship - Evolving Reservoir Meta-RL - Inria, Bordeaux, France

by Xavier Hinaut

Please find below an offer for an intership from February/March to July/August 2024 in Mnemosyne team at Inria, Bordeaux, France # Title Title: Evolving Reservoirs for Meta Reinforcement Learning # Supervision Xavier Hinaut, Mnemosyne team, Inria Bordeaux Potential collaboration with Clément Moulin-Frier (Flowers Team) & Pierrick Legrand (Astral Team) # Duration: 6 months, spring/summer 2024. Level: Master 2 research internship # Keywords: meta reinforcement learning, reservoir computing, evolutionary computation, neuroevolution, simulation environments, scientific programming with Python. # Research team & internship location Mnémosyne Team: Inria Bordeaux Sud-Ouest, LABRI & Institut des Maladies Neurodégénératives (Centre Broca Aquitaine, Carreire campus) https://team.inria.fr/mnemosyne # Project How can a reinforcement learning (RL) agent autonomously learn a diversity of skills with minimal external supervision? Recent works have shown that a single goal-conditioned policy can be trained to solve multiple tasks in parallel (see e.g. Colas et al., 2021 for a recent review). Other works in Meta Reinforcement Learning (Meta-RL) have shown that artificial agents can “learn how to learn” (hence the term meta-learning) multiple skills without having access to goal-related information (see e.g. Weng, 2019 for an introduction). Meta-RL aims at equipping agents with the ability to generalize to tasks or environments that have not been encountered during training. Two nested processes of adaptation are traditionally considered: an outer adaptation loop optimizing the hyperparameters of an inner adaptation loop. The inner adaptation loop can be a standard RL algorithm operating on a given environment. The outer loop is tuning the hyperparameters of the inner loop such that it performs well on a wide distribution of environments. The end result of this nested training process is an algorithm that learns how to learn, i.e. learns how to adapt to tasks that have never been encountered during training. Works differ in the optimization technique and adaptation mechanism they consider in the two loops. The outer loop often optimizes higher-level structures such as the architecture of a neural network (Baker et al., 2017), the morphology (Gupta et al., 2021), a curiosity module providing intrinsic rewards (Alet et al., 2020) or plasticity rules (Najarro & Risi, 2021), employing either evolutionary or gradient-based optimization. In parallel, Reservoir Computing (RC, (Jaeger 2001+)) is particularly well suited for extracting information from data stream with complex temporal dynamics (e.g. weather forecast or language). It is a machine learning paradigm on sequential data where a recurrent neural network is only partially trained (e.g. only training a linear readout). One of the major interests of these recurrent neural networks is their reduced computational cost and the possibility to learn both in on-line and off-line fashion. They have been successfully applied on a wide variety of tasks, from prediction/generation of chaotic timeseries to discrimination of audio sequences, such as bird song recognition. They offer de facto a fast, simple yet efficient way to train RNNs. This "reservoir of computations" works thanks to random projections in large dimensions, and is thus similar to temporal Support Vector Machines (SVM). In this internship, we will explore how Reservoir Computing can be leveraged in the context of Meta-RL. In this context, we will consider evolving the architecture of a reservoir for performing well on a wide range of RL tasks (see Seoane, 2019 for theoretical arguments on this evolutionary perspective). For this aim, we will use the ReservoirPy Python library developed in the Mnemosyne team (Trouvain & Hinaut, 2022) that we will interface with evolutionary computation algorithms (e.g. Tang et al., 2022). The project will involve: ● Designing of a wide range of RL tasks in an existing simulation environment (ex MuJoCo) ● Learning how to learn multiple tasks without access to the goal information (i.e. meta-RL with a reservoir). ● Evolve reservoir architectures for meta-RL with evolutionary methods # How to apply Contact xavier.hinaut(a)inria.fr with a CV and letter of motivation. We also recommend sending documents or reports describing previous projects you have been working on (even if they are not directly related to the topic), as well as your grades and links to some of your code repositories. # Requirements We are looking for highly motivated MSc students (Master II). Programming skills and prior experience with Python and deep learning frameworks (Pytorch, Tensorflow) are expected. # Other internship offers https://github.com/neuronalX/internships/ # References Alet, F., Schneider, M. F., Lozano-Perez, T., & Kaelbling, L. P. (2020). Meta-learning curiosity algorithms. International Conference on Learning Representations (ICLR 2020). https://openreview.net/forum?id=BygdyxHFDS Baker, B., Gupta, O., Naik, N., & Raskar, R. (2017). Designing neural network architectures using reinforcement learning. ArXiv:1611.02167 [Cs]. http://arxiv.org/abs/1611.02167 Botvinick, M., Ritter, S., Wang, J. X., Kurth-Nelson, Z., Blundell, C., & Hassabis, D. (2019). Reinforcement Learning, Fast and Slow. Trends in Cognitive Sciences, 23(5), 408–422. https://doi.org/10.1016/j.tics.2019.02.006 Colas, C., Karch, T., Sigaud, O., & Oudeyer, P.-Y. (2021). Intrinsically Motivated Goal-Conditioned Reinforcement Learning: A Short Survey. ArXiv:2012.09830 [Cs]. http://arxiv.org/abs/2012.09830 Gupta, A., Savarese, S., Ganguli, S., & Fei-Fei, L. (2021). Embodied intelligence via learning and evolution. Nature Communications, 12(1), 5721. https://doi.org/10.1038/s41467-021-25874-z Jaeger, H. (2007). Echo state network. Scholarpedia, 2(9), 2330. https://doi.org/10.4249/scholarpedia.2330 Najarro, E., & Risi, S. (2021). Meta-Learning through Hebbian Plasticity in Random Networks. ArXiv:2007.02686 [Cs]. http://arxiv.org/abs/2007.02686 Sakemi, Y., Morino, K., Leleu, T., & Aihara, K. (2020). Model-size reduction for reservoir computing by concatenating internal states through time. Scientific Reports, 10(1), Article 1. https://doi.org/10.1038/s41598-020-78725-0 Seoane, L. F. (2019). Evolutionary aspects of reservoir computing. Philosophical Transactions of the Royal Society B: Biological Sciences, 374(1774), 20180377. https://doi.org/10.1098/rstb.2018.0377 Tang, Y., Tian, Y., & Ha, D. (2022). EvoJAX: Hardware-Accelerated Neuroevolution. Proceedings of the Genetic and Evolutionary Computation Conference Companion, 308–311. https://doi.org/10.1145/3520304.3528770 Trouvain, N., & Hinaut, X. (2022). reservoirpy: A Simple and Flexible Reservoir Computing Tool in Python. https://hal.inria.fr/hal-03699931 https://github.com/reservoirpy/reservoirpy Weng, L. (2019, June 23). Meta Reinforcement Learning. https://lilianweng.github.io/posts/2019-06-23-meta-rl/ Xavier Hinaut Inria Research Scientist www.xavierhinaut.com -- +33 5 33 51 48 01 Mnemosyne team, Inria, Bordeaux, France -- https://team.inria.fr/mnemosyne & LaBRI, Bordeaux University -- https://www4.labri.fr/en/formal-methods-and-models & IMN (Neurodegeneratives Diseases Institute) -- http://www.imn-bordeaux.org/en --- Our Reservoir Computing library: https://github.com/reservoirpy/reservoirpy

Dec. 1, 2023

Postdoc Position in Computational/Theoretical Neuroscience (Western University)

by Lyle Muller

The lab of Lyle Muller (http://mullerlab.ca) at Western University (London, Ontario) has a postdoctoral position available in computational and theoretical neuroscience. This position will involve collaboration between our laboratory and researchers with expertise in advanced methods of brain imaging (Mark Schnitzer, Stanford), neuroengineering (Duygu Kuzum, UCSD), theoretical neuroscience (Todd Coleman, Stanford), and neurophysiology of visual perception (John Reynolds, Salk Institute for Biological Studies). In collaboration with this multi-disciplinary team, this researcher will apply new signal processing techniques for multisite spatiotemporal data to understand cortical dynamics during visual perception. This project will also involve development of spiking network models to understand the mechanisms underlying observed activity patterns. The project may include intermittent travel between labs to present results and facilitate collaborative work. This research will involve in-depth analyses of neural recordings and computational modeling. Prior experience with scientific computing languages (MATLAB, Julia, or Python) and signal processing is preferred. Western University offers an excellent research environment and support. Candidates can write to lmuller2(a)uwo.ca<mailto:lmuller2@uwo.ca> with a CV. Selected applicants will be interviewed on a rolling basis starting immediately. Both the involved labs and the University are deeply committed to fostering diversity and scientific outreach. Applications from underrepresented groups are strongly encouraged, and any interested applicants are welcomed to write the PI directly with questions about the research environment. -- Lyle Muller http://mullerlab.ca

Dec. 1, 2023

Postdoc Fellow Position in EEG at UCL

by Fabrizi, Lorenzo

🔬🧠👶 Exciting opportunity! Postdoc research fellow wanted to join a new collaborative research project at UCL's Medawar Pain and Somatosensory Labs, UCLH and Birkbeck BabyLab/ToddlerLab<https://cbcd.bbk.ac.uk/toddlerlab>, led by Dr. Lorenzo Fabrizi<https://profiles.ucl.ac.uk/1290>, Dr. Judith Meek<https://www.uclh.nhs.uk/our-services/find-consultant/dr-judith-meek> and Prof. Emily Jones<https://cbcd.bbk.ac.uk/people/scientificstaff/emily-jones>. Investigate the connection between early sensory experiences and long-term sensory processing in preterm-born neonates and children. Use EEG and novel sensory testing to identify individuals at risk of sensory challenges. Collaborate with experts in preterm brain imaging and neurodevelopment. Requires a background in Medical Physics, Bioengineering, Computational Neuroscience, or related fields, proficiency in brain imaging and programming, and strong communication skills. Apply here<https://www.ucl.ac.uk/work-at-ucl/search-ucl-jobs/details?jobId=17017&jobTi…>. Deadline 2nd of January 2024. Lorenzo Fabrizi, PhD Associate Professor Medical Research Foundation Fellow Neuroscience, Physiology and Pharmacology Department Room G17A, Medawar Building Bloomsbury Campus University College London Gower St, London WC1E 6BT office tel. 02031081888 (int. 51888)

Nov. 30, 2023

Postdoctoral Position in modelling the impact of interoceptive signals on neural dynamics and human cognition. ENS Paris

by Boris

Postdoctoral Position in modelling the impact of interoceptive signals on neural dynamics and human cognition. Group for Neural Theory and LNC2, Ecole Normale Superieure, Paris. A post-doctoral position in theoretical neuroscience is open to explore the impact of cardiac inputs on cortical dynamics. Understanding the role of internal states in human cognition has become a hot topic, with a wealth of experimental results but limited attempts at analyzing the computations that underlie the link between bodily organs and brain. Our particular focus is on elucidating how the different mechanisms for heart-to-cortex coupling (e.g., phase-resetting, gating, phasic arousal,..) can account for human behavioral and neural data, from somatosensory detection to more high-level concepts such as self-relevance, using data-based dynamical models. The ideal candidate should have a doctoral degree in a quantitative discipline and previous experience with computational approaches to neural functions, excellent programming skills coupled with an interest for, and a willingness to learn about, interoception and its influences on neural dynamics and cognition. The candidate will have a unique opportunity to join the project at its very inception and take a key role in the technical and conceptual development. The position is available for 3 years. The postdoctoral fellow will be based at the Group for Neural Theory <https://gnt.dec.ens.fr/en> and LNC2 <https://lnc2.dec.ens.fr/en> , in Boris Gutkin’s team <https://lnc2.dec.ens.fr/en/teams/mathematics-neural-circuits> with strong interactions with Catherine Tallon-Baudry’s team. LNC2 is located in the center of Paris within the Cognitive Science Department <https://cognition.ens.fr/en> at Ecole Normale Supérieure <https://www.ens.psl.eu/en>, with numerous opportunities to interact with the Paris scientific community at large, in a stimulating and supportive work environment. Group for Neural Theory provides a rich environment and local community for theoretical neuroscience. Lab life is in English, speaking French is not a requirement. Salary according to experience and French regulations with health and social benefits. Informal enquiries and applications (CV, motivation letter and contacts for reference) should be sent boris.gutkin(a)gmail.com <mailto:boris.gutkin@gmail.com> and catherine.tallon-baudry(a)ens.psl.eu <mailto:catherine.tallon-baudry@ens.psl.eu>. Applications will be evaluated on the fly, the position remaining open until filled.

Nov. 30, 2023

Fully-funded PhD in machine learning for medical imaging (Indiana University, US)

by chigurupatisreekar＠gmail.com

The Garyfallidis Research Group (grg.luddy.indiana.edu) at the Luddy School of Informatics, Computing, and Engineering has open PhD positions on the topic of artificial general intelligence for medical applications. The appointment starts Fall 2024. The PhD student will join a dynamic and interdisciplinary team that includes engineers, neuroscientists, and cognitive scientists. The PhD applicants will work with Prof. Eleftherios Garyfallidis on various areas of research, including machine learning, medical imaging and/or scientific visualization. Prof. Garyfallidis' team collaborates with more than 300 research labs across the US. His research group is also the headquarters for the DIPY and FURY organizations deploying research software in thousands of labs across the globe. Minimum Qualifications: A BSc (or equivalent) is required in engineering, computer science, statistics, physics, mathematics or neuroscience or related fields; and solid programming skills necessary to handle big data and develop large scale simulations. Knowledge of Python and C is a plus. Knowledge of graphic shaders is a bonus. Previous experience in open-source development is not required but good to have. Questions and CV should be sent to elef(a)indiana.edu. Interviews will take place after officially applying here https://graduate.indiana.edu/admissions/apply.html#/login The specific track is Neuroengineering. Note that the deadline for Fall 2024 is December 15 2023. Indiana University is an R01 (research level 1) university with multiple campuses. We are situated on the Bloomington campus that has over 50K students. The GRG is part of a new engineering department that focuses entirely on building intelligent systems. Students receive a fellowship (working as research assistants or teaching assistants) for the first 4 years of the PhD program. The students are encouraged to get internships in industry during the summer months (optional). This fellowship package guarantees recipients a full stipend, fee remission, and student health insurance during a 4 year period as you work towards the Ph.D. degree. Lab: https://grg.luddy.indiana.edu Twitter: https://twitter.com/dipymri

Nov. 28, 2023

Two postdoc openings on computational analysis and modelling of dynamic functional connectivity in Alzheimer's Disease (Strasbourg, FunSy team, France)

by Demian Battaglia

We are happy to announce two postdoc openings in the group of Dr. Demian Battaglia, within the newly created Functional System Dynamics team at University of Strasbourg. The recruited postdocs will work as well in close collaboration with Dr. Romain Goutagny in Strasbourg and Dr. Viktor Jirsa at the Institute for Systems Neuroscience in Marseille. Both positions will focus on investigating the link between dynamics of large-scale functional connectivity and cognition: first, by assessing that the maintenance of a structured and fluid dynamics of Functional Connectivity is a predictor of cognitive performance and of its resilience despite progressing neurodegeneration; second by showing that interventions aiming at restoring "healthy" brain dynamics can rescue cognitive performance. POSITION START: as early as possible (ideally, before April 2024) POSTDOC DURATION: 24 months - TWO POSITIONS AVAILABLE Specifically the first position will delve with the study of dynamic Functional Connectivity variations and their correlation with variations of cognitive performance in longitudinal studies of aging and Alzheimer's disease cohorts (human resting state fMRI, physiological markers, cognitive profiling...). The second position will delve with the characterization of the effects of non-invasive visual gamma flickering stimulation (vGENUS) on electrophysiological activity in AD mutant mice and the modelling of the mechanisms behind these effects via virtual mouse mean-field whole-brain simulations. We are seeking for candidates, with previous experience in computational neuroscience or computer science, physics or mathematics training with strong motivation to transit toward neuroscience. Previous experience with whole-brain modelling or dynamic functional connectivity analyses would be a plus, although not strictly required. Interested candidates could have hybrid data analysis/computational modelling projects. We expect fluent programming skills (e.g., Python, C/C++, Matlab), good communication skills in English (oral and written), the ability to work well in an international team, curiosity and open-mindedness (more than applying existing methods, we tend to forge original ones, shaped by the specific scientific questions we work on). The candidate will integrate the Functional System Dynamics team (FunSy, https://funsyteam.org) at University of Strasbourg, which is a mixed theoretical and experimental group focusing on the functional roles of multi-scale system's level dynamics. FunSy research aims at linking dynamics to information processing, behavior and cognition in the healthy and pathological brain, exploring projects in rodent, non-human primate or human neuroscience as well as pure theory. The present postdoc openings are funded by the Foundation "Vaincre Alzheimer" and the French National Priority Research Investment Program "PEPR Digital Health". Strict interactions are to be expected with other partners in this initiative ("Brain Health Trajectories"), with frequent visits at Viktor Jirsa's lab at Aix-Marseille University, or other researchers at Neurospin in Saclay (Paris South) or Grenoble. Strasbourg University is home of an emergent and active community in neuroscience, from molecules and cells to whole brain imaging and behavior, in relation with clinics. The proximity with Germany and Switzerland is an asset allowing interactions with transnational computational neuroscience centers. Strasbourg, one of European Union's capitals, is a fascinating city with a rich student and cultural life, well connected via high-speed train to important European cities (e.g. less than two hours from Paris) DEADLINE FOR APPLICATION: December 24th 2023 Please send your application, including CV, motivation letter and representative publications (preprints accepted) electronically to Dr. Demian Battaglia. In addition, contacts of at least two academic references must be communicated (contact email or recommendation letter attachments). Pre-selected candidates will be interviewed over Zoom or invited to give a talk if possible. We wish to promote the recruitment of strong candidates from minority groups in STEM research. Feel free to inquire for additional requests of information. CONTACT: Demian Battaglia (dbattaglia(a)unistra.fr or demian.battaglia(a)univ-amu.fr)

Nov. 27, 2023

PhD student at Instituto de Neurociencias Alicante

by Encarni Marcos Sanmartin

*A fully funded PhD (4 years) is available at Instituto de Neurociencias Alicante. * The student will be part of a project called BODIES that aims at understanding the neuronal mechanisms underlying the integration of action and perception during decision making. To achieve the goal of BODIES, experiments will be performed in mice. Neuronal activity will be recorded from a prefrontal network while animals perform a navigational task in virtual reality. Further, the activity of neurons of specific populations within this network will be manipulated through optogenetics, to investigate their causal contribution to behavior. During the project, a computational model will be developed to understand and describe the observed dynamics and to make further predictions for future experiments. Technical support is provided throughout the duration of the project. The PhD student will be enrolled in the PhD program in Neuroscience of the Universidad Miguel Hernández de Elche, under the supervision of Dr. Encarni Marcos. *The call will open this week and close shortly after:* https://otri.umh.es/2023/11/15/ayudas-para-la-formacion-de-personal-investi… The selected candidate will join the PhD program and the lab by the beginning of next year. If you are interested in this position, please, write to emarcos(a)umh.es Candidates must - Have a master’s degree in Neuroscience, physics, computer science, mathematics, psychology or related areas. - Be able to work independently. - Have programming skills. - Have excellent communication skills in English. - Work well in team. The Instituto de Neurociencias Alicante is a center of excellence that regularly organizes training courses for PhD students that are free of charge, weekly scientific lectures with well-known international speakers and internal seminars to promote collaboration within the Institute and offers financial support to assist national and international conferences for PhD students. The Institute is located in Sant Joan d'Alacant, a nice town near the beach and close to Alicante. Please feel free to get in touch with me if you have any questions. Dr Encarni Marcos Instituto de Neurociencias Alicante Sant Joan d’Alacant, Spain E-mail: emarcos(a)umh.es

Nov. 27, 2023

Three Faculty Positions in AI in Biology at VIB and KU Leuven

by pedro goncalves

Dear all, The VIB Center for AI & Computational Biology (VIB.AI) and KU Leuven are searching for three Principal Investigators to join their faculty (Group Leader at VIB, Professor at KU Leuven). We are particularly interested in recruiting faculty members who use and develop artificial intelligence methods and mechanistic mathematical models to address fundamental questions in biology. We welcome applications across all domains of machine learning, artificial intelligence, and associated fields. Examples of research topics include but are not limited to: development of new AI architectures for biology and hybrid models that combine deep learning with mechanistic models; foundation models of genome regulation using single-cell and spatial multi-omics data; AI-based modeling of protein structure and protein interaction networks; AI-based modeling of cell morphology and tissue function using imaging and computer vision; AI models of disease and digital twin applications. Biological applications are broad: microorganisms, plant biology, biodiversity and ecology, neuroscience, cancer, and immunology. We also welcome applicants with applied projects, including synthetic biology, AI-driven experiments (experiment-in-the-loop), and bio-engineering. This position comes with full salary and core funding (internationally competitive package) that is renewable for multiple additional 5-year periods, and access to state-of-the-art research and top-notch support core facilities, as well as support to attract talented PhD students and postdocs from across the world. More details about the positions at https://vibai.sites.vib.be/en/opportunities#/job-description/66534 Best wishes, Pedro --------------- Pedro Gonçalves Principal Investigator at NERF, a joint research initiative by imec, VIB, and KU Leuven Co-affiliated PI at VIB Center for AI & Computational Biology https://goncalveslab.sites.vib.be/en

Nov. 27, 2023

Call for Papers - From Animals to Animats: 17th International Conference on the Simulation of Adaptive Behavior (SAB 2024)

by Jeffrey L Krichmar

Dear Comp-Neuro colleagues, CALL FOR PAPERS - SAB 2024 ------------------------------------------- The Simulation of Adaptive Behavior (SAB) interdisciplinary conference brings together researchers in artificial intelligence, artificial life, computer science, cybernetics, ethology, evolutionary biology, neuroscience, robotics, and many other fields, to further our understanding of the behaviors and underlying mechanisms that allow natural animals and artificial agents to adapt and survive in complex, dynamic and uncertain environments. Animats denote the connection between animals and synthetic agents. The conference focuses on models and experiments designed to help characterize and compare various organizational principles and architectures underlying adaptive behavior in real animals and in animats. SAB 2024 solicits contributions dealing with any aspect of adaptive behavior. We especially invite submissions from researchers who connect biology and natural behavior to synthetic agents. Conference papers are peer-reviewed and accepted papers will be published in “From Animals to Animats 17. SAB 2024.” Lecture Notes in Artificial Intelligence, Springer Nature. Venue -------- CalIT2 Auditorium University of California, Irvine Irvine, CA 92697 USA Important Dates ---------------------- April 1, 2024: Deadline for paper submission April 15, 2024: Deadline for Workshop and tutorial proposals June15, 2024: Notice of acceptance June 30, 2024: Camera-ready papers September 9-12: Conference dates More details can be found at: https://sab2024.socsci.uci.edu/call-for-papers Best regards, Jeff Krichmar Department of Cognitive Sciences 2328 Social & Behavioral Sciences Gateway University of California, Irvine Irvine, CA 92697-5100 jkrichma(a)uci.edu http://www.socsci.uci.edu/~jkrichma

Nov. 26, 2023

looking for a postdoc on modeling / optimization of brain stimulation

by Dana Brooks

Postdoctoral Research Fellow at UMass Boston A postdoctoral fellow position is available in the Brain Stimulation & Simulation Lab <https://www1.coe.neu.edu/%7Erampersad/index.html>, directed by Dr. Sumientra Rampersad, in the Department of Physics at the University of Massachusetts Boston. Our lab focuses on investigating electromagnetic brain stimulation through computational methods and experiments with healthy volunteers. This position is funded by a newly awarded 5-year NIH R01 <https://reporter.nih.gov/project-details/10719222> grant with the goal to investigate a novel form of brain stimulation called transcranial temporal interference stimulation (TIS) and to optimize and translate it into an effective and efficient neuromodulation method for academic research and clinical practice. The fellow will develop novel optimization methods for high-density TIS and supervise a PhD student who will conduct simulations and optimizations using finite element modeling. The goal is to provide noninvasive and spatially specific treatment options to patients with brain disorders resistant to existing approaches. You will join a multidisciplinary team with expertise in computational modeling and optimization, human and primate electrophysiology, cellular and systems neuroscience, and biomedical engineering. You will also have the opportunity to work on other cutting-edge projects involving several brain stimulation methods including tCS, TMS, ECoG, sEEG and TTF (see lab website <https://www1.coe.neu.edu/%7Erampersad/index.html> for examples). Our collaborators at Northeastern University, the University of Utah, Harvard University, MGH, and the University of Washington, provide us access to specialized software and unique clinical data. The fellow will be mentored by Dr. Rampersad (UMass Boston) and Dr. Dana Brooks (Northeastern). This is an outstanding career development opportunity to work with leaders in the field of brain stimulation with an exceptional record of collaboration and mentoring. Required qualifications include a PhD in physics, math, electrical, biomedical engineering, or equivalent, expertise in either brain stimulation modeling or computational optimization methods (preferred both), a track record of conference presentations and peer-reviewed publications, strong verbal and written communication skills, and proficiency in Matlab or Python. This is a one-year appointment with the possibility of extension provided satisfactory progress is made. To apply, send a cover letter and CV to Dr. Rampersad (sumientra.rampersad(a)umb.edu) with the subject “TIS postdoc”. Please describe in detail your experience with optimization, brain stimulation, FEM, coding, project management, and mentoring students.

Nov. 26, 2023