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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 13 days ago
mathematical background. - Familiarity with deep learning frameworks such as PyTorch. - Commitment, team working and a critical mind. - Fluent verbal and written communication skills in English
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applied methodologies in Data and Image Analysis, Computational Imaging, Statistical Learning, Uncertainty Quantification, Robust Estimation, and Deep Neural Networks. The group combines expertise in
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). Completed academic courses in AI or machine learning. We consider it an advantage if you bring experience with Reinforcement Learning, Deep Learning and/or Explainable AI, demonstrated for example through
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generalizable, learning-based frameworks for dexterous robotic manipulation that are robust to environmental variability and transferable across diverse underwater robotic platforms. As a PhD in this position
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proficiency in Python, R, or MATLAB. Experience with Deep Learning frameworks (PyTorch, TensorFlow) and LLM APIs is an asset. Communication: Fluent English skills, both written and spoken, with a demonstrated
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complex interaction patterns that may carry important biological information. By integrating deep learning, genome-wide simulations, functional genomics, and large-scale biobank data, AI:GENOMIX aims
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PhD Position - Marie Curie network ON-Tract: Protein engineering of enzymes: in vitro directed evolution and machine learning-based elaboration of biocatalysis for synthesis. A doctoral position is
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. Additional qualifications Experience with one or more of the following areas is meriting: Bayesian statistics, mathematical modelling, probabilistic machine learning, deep learning, large language models
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Supervisor: Dr. Kamila Maria Jozwik, Jozwik lab PhD fees status: Home fees only (https://www.postgraduate.study.cam.ac.uk/finance/fees/what-my-fee-status ), 4 years Start date: October 2026 The
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across both surface and subsurface layers. This includes constructing robust feature extraction pipelines, attention-based fusion architectures, and deep learning models that accurately characterize cracks