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postgraduate levels. We welcome applications from candidates whose research aligns with any area of Artificial Intelligence, including but not limited to Mathematical Theories for AI: Algorithmic Optimizations
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or AI – for example, stochastic optimization and sampling algorithms, high dimensional probability, diffusion models, statistical learning theory or teaching courses on the mathematical foundations of ML
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systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and algorithms. Our research integrates expertise from machine learning, optimization
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international collaborators. The group's interests cover a wide range of areas including extremal and probabilistic combinatorics, combinatorial optimization, graph theory, game theory, algorithmics and geometry
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1 Aug 2026 Job Information Organisation/Company CNRS Department Laboratoire de physique théorique et hautes énergies Research Field Computer science Mathematics » Algorithms Researcher Profile
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deep neural networks to guide the development of algorithmic paradigms aimed at combining statistical optimality with computational efficiency. Reinforcement Learning through Stochastic Control. We will
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"Gheorghe Mihoc - Caius Iacob" Institute of Statistical Mathematics and Applied Mathematics of Romanian Academy | Romania | 2 months ago
machine learning algorithms, using specific software libraries (PyTorch, TensorFlow, scikit-learn or equivalent), processing and visualizing biomedical data, and using software versioning and collaboration
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campus. We are particularly interested in researchers working in mathematical artificial intelligence, broadly construed. This includes, but is not limited to: machine learning algorithms, formal proof
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. This includes, but is not limited to: machine learning algorithms, formal proof assistants, and large language models. Areas of interest for possible collaborations include but are not limited to: topological
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or AI – for example, stochastic optimization and sampling algorithms, high dimensional probability, diffusion models, statistical learning theory or teaching courses on the mathematical foundations of ML