September 2023 - Comp-neuro - lists.cnsorg.org

postdoc position announcement at INMED (Marseille)
by Lorenzo Fontolan Sept. 1, 2023

Sept. 1, 2023

An ERC-funded postdoctoral position is available in the Cossart lab at the Mediterranean Institute of Neurobiology (INSERM, Aix-Marseille University, Marseille, France) to work in a collaborative, interdisciplinary, and friendly environment (by the Calanques). The Cossart lab aims at understanding memory circuits in the brain and describing how they develop in health and disease. To this aim, we use cutting-edge optical, computational, and data analysis methods. The candidate will apply their skills to extract information from our datasets, build computational models to make predictions, and work in close collaboration with experimentalists. The candidate will be co-supervised by Dr. Lorenzo Fontolan, a computational neuroscientist who recently started his research group at Inmed. Starting date fall/winter 2023. We especially invite applications from prospective postdocs with backgrounds in physics, applied mathematics, and electrical engineering. To apply, please send a CV, a short statement containing research interests and previous experience, and two names of references with contact information to: *rosa.cossart(a)inserm.fr <rosa.cossart(a)inserm.fr> *and* lorenzo.fontolan(a)inserm.fr <lorenzo.fontolan(a)inserm.fr>* Lorenzo Fontolan -- Group Leader *Turing Centre for Living Systems (CENTURI) Institut de Neurobiologie de la Méditerranée & Centre de Physique Théorique Université Aix-Marseille, France* *https://www.inmed.fr/neural-computation-and-dynamics <https://www.inmed.fr/neural-computation-and-dynamics>* *https://centuri-livingsystems.org/l-fontolan/ <https://centuri-livingsystems.org/l-fontolan/>*

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Postdoc position in Bilbao on open-ended network self-organization
by Miguel Aguilera Sept. 1, 2023

Sept. 1, 2023

Hi all, We are offering one postdoc position on self-organized network modelling at the Basque Center for Applied Mathematics in Bilbao (Basque Country, Spain) under the supervision of MIguel Aguilera. DETAILS: **Postdoctoral Fellow on open-ended, self-organized, bio-inspired networks** Topics: One of the outstanding challenges in modelling living and cognitive systems is to capture their ability to continuously adapt and develop. Our behavioural responses are not fixed but driven instead by a complex ecology, composed of myriads of fluid and inconspicuous neurodynamical patterns that have slowly grown on us. AI models like neural networks can capture complex behaviours, but often this open-ended, liquid and ongoing reconfiguration is incredibly challenging to reproduce with static topologies. In contrast, liquid neural networks (or 'liquid brains') are a widespread class of networks with a particular feature: nodes (which may represent 'neurons' or 'agents' and typically all share identical rules) not only process information from neighbour nodes, but they also dynamically modify their network connections, e.g. by moving in space. These networks show how neural-like processing typically associated with static physiological networks can also emerge from fluid collective interaction, dynamically exploring configuration spaces beyond standard connection weight changes. Examples of this kind of model include collective decision-making and fluctuations in ant colonies or idiotypic cascades in immune networks. Objective: The aim of this project is to develop a theory of learning in liquid brains, focused on two aspects: 1) How do liquid brains learn? How is this process different from static neural networks?, and 2) what is the adaptive potential of liquid brains when they are embodied as an agent in interaction with a changing external environment? Answering these questions has the potential to extend the idea of liquid brains from a theoretically deep and intriguing concept to a useful tool available to the machine learning community. Specifically, liquid brains could afford more open-ended, self-improving systems, exploiting fluid reconfiguration of nodes as an adaptive dimension which is generally unexplored. This could also allow modes of learning that avoid catastrophic forgetting, as reconfigurations in the network are based on reversible movement patterns. In terms of technology transfer, this advances can also have important implications for new paradigms like edge computing. PI in charge: Miguel Aguilera <https://maguilera.net/> Salary and conditions: The gross annual salary of the Fellowship will be 29.120€ - 35.360€ according to experience. Additionally, we offer a moving allowance up to 2.000€. Should the researcher have a family at the time of recruitment: 1. 2.000€ gross in a single payment will be offered (you must be married-official register or with children and the certificate to prove it must be sent). 2. 1.200€ gross per year/per child (up to 2 children) will be offered (the certificate to prove it must be sent). Contract and offer: 1 + 1 years Deadline: 8TH September 2023, 14:00 CET How to apply: Applications must be submitted on-line at: https://joboffers.bcamath.org/apply/ic2023-08-postdoctoral-fellow-in-open-e… **Scientific Profile Requested** Requirements: PhD in Physics, Engineering, Computer Science, Maths, Artificial Intelligence, Computational Neuroscience and related fields. Skills and track-record: * Modelling experience with complex or neural network models, e.g. Hopfield networks, Ising models, Boltzmann machines, sandpile models, flock/swarm/insect colony models. * Solid programming skills * Analytical study of stochastic processes * Demonstrated ability to work independently and as part of a collaborative research team. * Ability to present and publish research outcomes in spoken (talks) and written (papers) form. * Fluency in spoken and written English. Scientific Profile: The preferred candidate will have: * Strong background in complex systems, neural network modelling, biophysics, information theory and/or statistical mechanics * Interest to work in interdisciplinary research projects Application and Selection Process Formal Requirements: The selected candidate must have applied before the application deadline online at the webpage https://joboffers.bcamath.org/ The candidates that do not fulfil the mandatory requirements will not be evaluated with respect to their scientific profile. Additional documents could be requested during the evaluation process so as to check this fulfilment. Application: Required documents: * CV * Letter of interest * 2 recommendation letters * Statement of past and proposed future research (2-3 pages) Evaluation: Based on the provided application documents of each candidate, the evaluation committee will evaluate qualitatively: the adaption of the previous training and career to the profile offered, the recommendation letters, the main results achieved (papers, proceedings, etc.), the statement of past and proposed future research and other merits; taking in account the alignment of these items to the topic offered. Incorporation: As soon as possible -- Miguel Aguilera | Ikerbasque Research Fellow | BCAM – Basque Center for Applied Mathematics |https://maguilera.net

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Kempner Research Fellows
by Cengiz Pehlevan Sept. 1, 2023

Sept. 1, 2023

The Kempner Institute at Harvard University has opened the application for the next cohort of Kempner Research Fellows. The application is open now and will close on October 9, 2023. The 3 year fellowship provides a generous stipend, funding for research, and access to our GPU cluster. We seek candidates who are working on or more of the following areas: Foundations of intelligence, including mathematical and computational models of intelligence, cognitive theories of intelligence, and the neurobiological basis of intelligence. Applications of artificial intelligence, including natural language processing, visual scene processing and analysis, and to the mechanistic analysis of high dimensional neural and behavioral data. The study of these applications can either be from an engineering standpoint (e.g., development of new methodologies or advancing the state of the art) as well as from a scientific one (e.g., achieving a better understanding of deep learning). To be eligible, a candidate must be, at the earliest, in the final year of their Ph.D. training and at the latest, have received their PhD no earlier than 9/1/2021. See this link for more information and how to apply: https://www.harvard.edu/kempner-institute/opportunities/the-kempner-institu… Email KempnerInstitute(a)harvard.edu <mailto:KempnerInstitute@harvard.edu> for any questions about the position.

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