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Field
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to work within one of, or across, the four research themes: Learning with Structured & Geometric Models, Low Effective-dimensional Learning Models, Implicit Regularization, and Reinforcement Learning
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Requirements Research FieldComputer science » Computer systemsEducation LevelPhD or equivalent Skills/Qualifications Knowledge • Solid understanding of machine learning, deep learning, and modern AI techniques
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computational approaches to biological systems. Its core activity is the development of deep learning methods for protein design and optimization, with applications in biology and medicine. - activities: We
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partners. Qualifications Ph.D. in Computer Science, Applied Mathematics, or a related field. Strong publication record in machine learning, with preference for expertise in representation learning, deep
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mechanisms available in multi-parametric MRI, we aim to establish deep learning models that predict biomarkers of diseases progression and response to therapies, with applications in brain tumours and neuro
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with a minimum of one year eligibility remaining. •Strong expertise in first-principles modeling or molecular simulations relevant to catalysis, and experience with machine learning models. •Deep
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) Experience in deep learning algorithms is a plus Ability to work in a highly international team and interdisciplinary project applicants are expected to have excellent language skills in English Opportunity
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models when faced with data drift, bias, and fairness challenges. The research will involve developing deep learning and synthetic data generation approaches and applying them to exemplar studies in
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.). Proficiency in programming languages such as Python, and experience with deep learning frameworks like TensorFlow, PyTorch, or JAX. In-depth understanding of transformer architectures, attention mechanisms, and
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Inria, the French national research institute for the digital sciences | Palaiseau, le de France | France | 5 days ago
, Pengwenlong Gu, Cedric Adjih, Paul Muhlethaler, Ahmed Serhrouchni, "DS-IRSA: A Deep Reinforcement Learning and Sensing Based IRSA" in IEEE Global Communications Conference - GLOBECOM 2023, Kuala Lumpour