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work will focus on identifying the mathematical knowledge and properties to guide hardware optimizations tailored to different environments. The optimizations range from algebraic optimizations (e.g
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. Your work will focus on identifying the mathematical knowledge and properties to guide hardware optimizations tailored to different environments. The optimizations range from algebraic optimizations (e.g
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over separation algebras, combining linear and affine resources (by the end of December); - Develop a novel update modality supporting non-frame-preserving updates for thread-local resources so that
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challenging properties of uncertainty, irregularity and mixed-modality. It will examine a range of models and techniques that go beyond Markovian approaches, including state-space models, tensor networks, and
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mixed-modality. It will examine a range of models and techniques that go beyond Markovian approaches, including state-space models, tensor networks, and machine learning frameworks such as recurrent
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Python with demonstrable familiarity with PyTorch, experience in working on shared codebases, excellent applied math skills (especially probability theory, matrix algebra, calculus). Beyond technical
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van Amsterdam. The KdV Institute offers a stimulating scientific environment in which research focuses mainly within the research programmes (1) Algebra, Geometry and Mathematical Physics, (2) Pure
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models that integrate data from quantum simulations and experiments, using techniques such as equivariant graph neural networks with tensor embeddings. We aim to train these methods in a closed-loop
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) Strong programming and quantitative skills, including coursework in multivariable calculus, matrix algebra, probability, and statistics (required) Previous experience estimating econometric models
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artificial intelligence. Good knowledge of linear algebra and optimization tools. Good programming skills in Python and Matlab. Good written and oral communication skills in English. Specific Requirements