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Sorbonne Université SIS (Sciences, Ingénierie, Santé) | Paris 15, le de France | France | 3 days ago
will focus on the following main tasks: 1) Machine-learn optimal reaction coordinates for the barnase-barstar complex, starting from ~0.1 millisecond high-dimensional MD (generated by a previous PhD
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Essential skills, knowledge and experience: Experience with machine/deep learning development Data-Centric AI Knowledge Notions of cybersecurity and networks are optional Spoken and written English Desirable
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, statistics and probabilities • data science, machine learning, artificial intelligence • optimisation • power system management, integration of renewables • energy forecasting Expected level in french : bon
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, including machine learning and language technologies, for the integration and analysis of clinical, advanced data harmonisation, and next generation research infrastructures. You will contribute to research
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) signal processing, machine/deep-learning and computational linguistics. The team mobilizes them to produce methodologically sound research in response to some of the challenges posed by the nature and
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4 Skills The candidate should preferably have a PhD-level background in quantum theory or machine learning, with strong experience in at least two of the following areas: • Knowledge distillation
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(graduated or close to graduation) in Computer Science, Computer Engineering, Artificial Intelligence, Machine Learning, Applied Mathematics, or related fields. Scientific curiosity and creative thinking
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problems, statistical learning and machine learning (machine learning, deep learning) - Knowledge of associated software development tools and environments: Python, PyTorch, Scikit-learn, Jax, Julia
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candidate will hold a PhD in geosciences, applied machine learning, data assimilation, or applied mathematics. The selection will be based on the following scientific and technical criteria: Experience in
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significant computational component. We strongly recommend a background in machine learning and coding. Applicants with a background in areas such as computational neuroscience, reinforcement learning, or deep