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simulations. Data-driven materials discovery: ML models for property prediction, materials design, or synthesis optimization. AI/ML methods development: Neural networks, graph neural networks (GNNs), generative
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Learning, particularly Graph Neural Networks, Transfer Learning, Deep Reinforcement Learning, and Transformer-based models, including hands-on implementation Strong understanding of machine learning models
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instances to solve new, yet similar, instances more efficiently than with general purpose algorithms such as Netwon`s method. In particular, we aim to develop a neural network architecture that will allow us
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mechanotherapeutics. 4. Neuromechanobiology: this area focuses on discovering the mechanobiological basis of neurons, neural networks, and the brain in memory, learning, cognitive function in health and
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 3 months ago
/logical rules with neural networks to increase accuracy and explainability. Where to apply Website https://jobs.inria.fr/public/classic/en/offres/2026-09730 Requirements Skills/Qualifications PhD in neuro
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interdisciplinary areas. Research fields of particular interest include, but not limited to: biomedical science and engineering veterinary science computer science and data science neuroscience and neural
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, or computational modelling, and you should be comfortable working with large datasets and modern deep learning frameworks. Experience with neural network design, optimisation techniques, or scalable computing is an
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wide range of academic fields, Ghent University is a logical choice for its staff and students. Image Processing and Interpretation (IPI, http://ipi.ugent.be ) is an imec research group at Ghent
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A Research Specialist position is available in the Princeton Neuroscience Institute at Princeton University. The Falkner lab (https://www.falknerlab.com/) focuses on neural circuit dynamics in
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building automated systems or ML pipelines – 15% Demonstrated experience implementing structured, scalable, or automated software systems. Evidence of experience with neural networks, LLMs, or training