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will be postdoc stipends in the following specific areas/centers of the department: * Algebraic and arithmetic geometry, applied algebra, combinatorics, geometric group theory, geometry and geometric
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programming in Python, R, or similar languages Background in machine learning or deep learning methods, including Graph Neural Network (GNN) Experience with Large Language Models (LLMs) applied to
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Are you interested in data-driven modelling and optimisation of District Heating Systems and can you contribute to the development of our Villum Synergy project, “ Recycling waste heat: Graph-based
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learning methods, including Graph Neural Network (GNN) Experience with Large Language Models (LLMs) applied to biological data collection, extraction, and standardization Knowledge of multi-omics integration
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develop a theory of spectroscopy with entangled surface plasmons. You will join the "Theory of light-matter interaction in quantum materials" group at DTU Physics. Check out our website here. About the
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responsibilities include bridging high-capacity generative representations and discrete world models with non-stationary, risk-sensitive reinforcement learning theory. To achieve this, the candidate will develop
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memory models; guarded cubical type theory; and Rocq formalizations. Tasks and Qualifications Applicants are expected to have a strong background in semantics, type theory, and Rocq formalization. The
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(e.g., AntiBERTy, AbLang2) Experience with structure prediction frameworks, geometric deep learning, or graph neural networks Familiarity with computational antibody/binder discovery or molecular
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survey research, together with a good understanding of behaviour change theory (e.g. COM-B, the Theory of Planned Behaviour, or the Value-Belief-Norm model) as applied to sustainability or circular
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; Probability Theory and Analysis. The list may be updated with new areas up until the deadline for application. As a successful candidate you are expected to: * shine in individual and collaborative