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be done via computer simulations, including Monte Carlo and molecular dynamics, combined with the use of statistical mechanics to predict e.g. phase transitions, nucleation rates, etc. The work will be
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The Machine Learning for Integrative Genomics team at Institut Pasteur, headed by Laura Cantini, works at the interface of machine learning and biology, developing innovative machine learning
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for managing smart cities. The team has gained substantial experience in machine learning for road traffic monitoring. They are now keen to thoroughly explore the additional opportunities presented by
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the Facility, they will be responsible for the proper operation of the in vivo imaging equipment of the Institute, training and assisting users to develop new protocols matching custom needs, supporting users in
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of MIXAP on teaching and learning. We aim for a qualitative and quantitative analysis with questionnaires for teachers and students, focus groups, videos, usage logs, etc. • Provide a list of
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motivated the development of Federated Learning (FL) [1,2], a framework for on-device collaborative training of machine learning models. FL algorithms like FedAvg [3] allow clients to train a common global
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. Required Skills and Candidate Profile The project is intended for a candidate with: ➢ Skills in medical image processing and deep learning adapted to clinical applications. ➢ A good knowledge of Python
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train robust machine learning (ML) algorithms without exchanging the actual data. The benefits of such a decentralized technology over personal and confidential data are multiple and already include some
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The Machine Learning for Integrative Genomics team at Institut Pasteur, headed by Laura Cantini, works at the interface of machine learning and biology, developing innovative machine learning
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computational framework, integrated with deep reinforcement learning (DRL) methodologies for both gene-level and edge-level perturbation control, represents a significant advancement in the computational toolkit