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Science, Applied Mathematics or a closely related field. A strong mathematical background (probability, linear algebra, optimisation) and proficiency in programming (preferably Python/Matlab) are essential. Prior
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development. Proven competence in probability, linear algebra, and statistical modelling is essential for this position. A strong interest in probabilistic modelling, latent-variable methods or Bayesian
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, especially in quantitative subjects • Strong Python skills and experience with deep learning frameworks, preferably PyTorch • Solid foundations in machine learning, statistics, linear algebra, and model
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Heidelberg Institute for Theoretical Studies (HITS gGmbH) | Heidelberg, Baden W rttemberg | Germany | about 5 hours ago
, theoretical physics, or a related discipline. Candidates should preferably have a strong theoretical background in machine learning, a solid foundation in linear algebra, and excellent programming skills
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Newtonian dynamics) and linear algebra (vectors and matrices). Experience working with numerical integration techniques and/or rigid body dynamics. Understanding of core networking principles (e.g. client
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, linear algebra, probability theory, (Bayesian) statistics, optimization and elementary graph theory Familiar with machine learning and deep learning Programming experience (Python or Julia) and their
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structured approach to problem-solving. Strong foundations in linear algebra, probability theory, and calculus are required. Solid skills in programming are required. Additional qualifications Experience with