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

Two postdoc positions in neuroAI
by Rui Ponte Costa 02 Aug '23

02 Aug '23

Dear all, We are seeking to appoint two Postdoctoral Research Scientists to join the Neural and Machine Learning Group part of the world-class Oxford Neuroscience and the Oxford Neurotheory initiative. You will carry out research at the intersection between computational neuroscience and machine learning, closely informed by experimental observations. More details here: shorturl.at/hkwG3<https://t.co/d7ugbOLFDV> and of our group: neuralml.github.io<https://neuralml.github.io/> Best wishes, Rui Ponte Costa Group Leader, Neural and Machine Learning group<https://neuralml.github.io/> Centre for Neural Circuits and Behaviour | Oxford Neurotheory Department of Physiology, Anatomy and Genetics Department of Experimental Psychology (honorary member) University of Oxford, UK

1 0

[Job][PhD] at Inria Bordeaux, FR: From Reservoir Transformers to BrainGPT
by Xavier Hinaut 02 Aug '23

02 Aug '23

PhD position available at Inria, Bordeaux, France: "From Reservoir Transformers to BrainGPT" # Keywords Transformers; Reservoir Computing; Computational Neuroscience # Duration & start date 3 years starting 1st October 2023 (estimated) # Contact & Application Informal contact/questions and application: email to xavier dot hinaut at inria dot fr Application & More info: https://recrutement.inria.fr/public/classic/en/offres/2023-06611 All positions available: https://github.com/neuronalX/phd-and-postdoc-positions Deadline: Applications will be considered on a rolling basis. A candidate will be selected as soon as a suitable profile is found. # Context This PhD thesis is part of the BrainGPT "Inria Exploratory Action" project. In the wake of the emergence of large-scale language models such as ChatGPT, the BrainGPT project is at the forefront of research in Artificial Intelligence and Computational Neuroscience. These models, although remarkably powerful, do not reflect the way in which our brains process and learn language. BrainGPT is rising to the challenge by focusing on the development of models that are more faithful to human cognitive functioning, inspired by data on brain activity during listening or reading. The ambition is to create more efficient models that are less dependent on intensive calculations and massive volumes of data. BrainGPT will open up new perspectives on our understanding of language and cognition. # Project and Work description The rapid rise in performance of language models, as demonstrated by the recent appeal of ChatGPT, is undeniable. However, the computational cost and environmental impact associated with such models are often overlooked [1]. These models rely on Transformers [2], which facilitate unsupervised learning on a large volume of data. These same models are used to predict brain activity from functional magnetic resonance imaging (fMRI) or magnetoencephalography (MEG), an application our team also exploits [3]. The main ambition of the BrainGPT project is to combine the explainability of mechanistic models with the predictive power of Transformers to analyze brain imaging data. Today, on the one hand we have explanatory but less predictive mechanistic models, such as those based on Reservoir Computing, and on the other hand, high-performance predictive models, but not explanatory, like Transformers. Our goal is to combine the best of these two approaches, to develop more efficient ("sample efficient") models inspired by Transformers, which more faithfully reflect the how the brain works, while improving the predictive power of mechanistic models. Towards this ambition, the BrainGPT project seeks to identify the key mechanisms that allow Transformers to predict brain activity. Furthermore, our project strives to build models that are more biologically plausible than Transformers, incorporating the most relevant components for predicting brain activity, while integrating constraints derived from human cognition studies. The long-term objectives of the BrainGPT project are as follows: - Making Transformers more biologically plausible, which could improve the prediction of brain activity by imaging (fMRI, MEG, etc.). - Proposing new perspectives and computing paradigms that do not rely exclusively on gradient backpropagation, given its high computational and energy cost. - Reducing the energy footprint of Transformers by minimizing the computational costs associated with their learning. In summary, the thesis will mainly consist of developing new bio-inspired models inspired by the mechanisms, learning methods, and emerging behaviors of Large Language Models (LLMs) and Transformers. Subsequently, in collaboration with our collaborators, these models will be tested to assess their ability to predict brain activity from imaging data. [1] Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021, March). On the dangers of stochastic parrots: Can language models be too big?🦜. In Proc. of the 2021 ACM conference on fairness, accountability, and transparency (pp. 610-623). [2] Vaswani, A. et al. (2017) Attention is all you need. In Proc. of Advances in neural information processing systems. [3] Oota, S. R., Trouvain, N., Alexandre, F., & Hinaut, X. (2023, August). MEG Encoding using Word Context Semantics in Listening Stories. In Proc. of INTERSPEECH 2023. # Skills Ideal Candidate Profile: - Holds an engineering or scientific degree and/or a PhD in digital sciences (computer science, automation, signal processing). - Has a first professional experience (6 months of internship or more) in Machine Learning and Python development. Especially in one or more of the following: Recurrent Neural Networks (in particular Reservoir Computing), Transformers, Large Language Models. - Possesses strong expertise in the scientific Python software and scientific stack (numpy/scipy). - Demonstrates a solid grasp of linear algebra concepts. - Proficiency in technical English is crucial, as it enables efficient collaboration with our international partners and effective presentations at conferences. - Has proven experience with version management, familiarity with Git, and proficiency in using the GitHub platform. # Advisor & Location Xavier Hinaut Inria Bordeaux & Institute for Neurodegenerative diseases (Pellegrin Hospital Campus, Bordeaux). Best regards, 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

1 0

[JOBS] Tenure Track Assistant Professor Intelligent Systems @ Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence at the University of Groningen, the Netherlands.
by Kerstin Bunte 02 Aug '23

02 Aug '23

Tenure Track Assistant Professor Intelligent Systems https://www.rug.nl/about-ug/work-with-us/job-opportunities/?details=00347-0… Closing date = 17-9-2023 We offer a Tenure Track Assistant Professor position with a research profile in Intelligent Systems within the Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence at the University of Groningen, The Netherlands. Intelligent Systems aim to give computers and cyber-physical systems the abilities to perceive, analyze and learn from data, make decisions, and enhance human capabilities. The research area has experienced tremendous growth in recent years, especially due to the increase in incorporation of intelligent systems into nearly every aspect of our lives. The societal relevance of sustainability, efficiency, robustness, understanding and guarantees of algorithms is gaining more and more importance, not only in software but also in combination with cyber-physical systems. Relevant aspects are: the theory of machine learning, artificial intelligence and the mathematics of computing (ACM Computing Classification System, 2012 Revision). For this position, possible research directions include (but are not limited to) the theory and application of novel machine learning and artificial intelligence methods with an emphasis on solving pressing open questions, such as efficiency, transparency and interpretability, fairness, the integration of multi-modal and complex heterogeneous data, causal inference, continuous machine learning, generative and dynamic systems. Complex problems demand an increasing amount of interdisciplinary and cross-domain knowledge. Therefore, backgrounds in information theory, probabilistic representations, causality and computational geometry are also welcome to apply. As Assistant Professor with a focus on research, you will: - set up and develop your own research line and research group - supervise PhD students - acquire external funding - promote the societal relevance of your research - teach in and contribute to the development of the degree programs of the School of Science and Engineering, especially courses with computing science content - contribute to the organization of the faculty, for example by participating in working groups and committees, in the domains of teaching, research and management. The position will be embedded in the Intelligent Systems research unit. Our research areas include biologically inspired computational modeling, the theory of machine learning and algorithmic development, as well as image processing with mathematical morphology, machine learning and AI. National and international research collaborations span a multitude of application domains, including medical sciences and health care, natural sciences (astronomy, bioinformatics), remote sensing, computer vision, robotics, smart industry and engineering. About the University of Groningen Since its foundation in 1614, the University of Groningen has enjoyed an international reputation as a dynamic and innovative center of higher education offering high-quality teaching and research. Balanced study and career paths in a wide variety of disciplines encourage currently more than 37,000 students and researchers to develop their own individual talents. Belonging to the best research universities in Europe and the top 100 universities in the world, the University of Groningen is truly an international place of knowledge. More information can be found at: https://www.rug.nl/about-ug/work-with-us/job-opportunities/?details=00347-0… Kind regards, Kerstin -- Kerstin Bunte, Associate Professor | Rosalind Franklin Fellow Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence Faculty of Science and Engineering University of Groningen P.O. Box 407, 9700 AK Groningen The Netherlands Email: k.bunte(a)rug.nl web: http://www.cs.rug.nl/~kbunte/

1 0

Advanced Electrophysiology Post-doc Position Available - Interventional Psychiatry Program, Minneapolis, MN, USA
by Hiba Sheheitli 01 Aug '23

01 Aug '23

*About the Job* Dr. Ziad Nahas (Interventional Psychiatry Lab) in the University of Minnesota Department of Psychiatry and Behavioral Sciences is seeking an outstanding candidate for a postdoctoral position to conduct and analyze the effects of neuromodulation on brain activity in mood disorders. Candidates should be passionate about advancing knowledge in the area of translational research of depressive disorders and other mental health conditions with a focus on invasive and non-invasive brain stimulation treatments. The position is available June 1, 2023, and funding is available for at least two years. *Qualifications* Required Qualifications: ●PhD in relevant field (neuroscience, psychology, cognitive science) ●Strong programming and/or data analysis skills. MATLAB or Python is essential. C/C++ (and derivatives), LabVIEW, and scripting languages could be useful. Prior experience with data acquisition hardware including behavioral as well as EEG would be helpful. Proficiency with Microsoft Office software ●Experience with neurophysiology (human or animal), psychophysics and/or brain stimulation is ideal. ●Excellent verbal and written communication skills, with specific documentation of those skills through published work and oral scientific presentations. ●Demonstrated ability to work independently and to independently overcome complex technical challenges. ●Interpersonal skills appropriate for collaboration in a very multidisciplinary and highly diverse environment. Preferred Qualifications: •Previous experience with psychiatric patient populations, the biological mechanisms of mental illness, and/or medical device development. •Signal processing and/or computer science background. •Previous experience with human functional imaging or electrophysiology. *About the Department* Dr. Ziad Nahas’s Lab is located primarily at the Interventional Psychiatry Program’s St. Louis Park clinic, with additional study activity at UMN and M Health Fairview sites on the UMN campus. Our work focuses on advancing translational research of mood dysregulation and depressive disorders. Specific interests and areas of expertise include: ● Improving patient care of treatment resistant disorders using neuromodulation techniques. ● Functional neuroimaging (fMRI), electroencephalography (EEG) and brain stimulation modalities: Transcranial magnetic stimulation (TMS), transcranial alternating current stimulation (tACS), vagus nerve stimulation (VNS), prefrontal cortical stimulation (PCS), deep brain stimulation (DBS), electroconvulsive therapy (ECT) and focal electrically administered seizure therapy (FEAST). ● Increasing equitable access to neuromodulation interventions for patient care. To learn more about Dr Ziad Nahas and the Department of Psychiatry & Behavioral Sciences, please visit: https://med.umn.edu/bio/psychiatry/ziad-nahas *Apply here: * https://hr.umn.edu/Jobs/Find-Job <https://hr.umn.edu/Jobs/Find-Job> (Click on External Candidates and search for 355003 to find the Job Posting)

1 0