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Field
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structure-preserving methods for mathematical models of physical systems, including topics such as geometric numerical integration, finite-volume and finite-element methods, variational formulations
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Skills/Qualifications Technical Skills: Advanced programming in Python, PyTorch, PyTorch Geometric or DGL. Version control (git), Linux and model training on GPU (reproducible experiments). Other
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/CAM programming and CNC machine tool operations. - Ability to read technical drawings. - Practical experience using/applying/interpreting drawings with geometric dimensioning and tolerances (GD&T
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operations. • Must possess thorough and complete understanding of Geometric Dimensioning and Tolerancing (GD&T). • Familiar with various manufacturing processes including, but not limited to, heat treatment
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Model over that pipeline — a model that does not just generate code, but predicts the consequences of an architectural decision: total cost of ownership, unintended side effects, latency and failure
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assistants, and large language models. Areas of interest for possible collaborations include but are not limited to: topological data analysis and topological machine learning; AI-assisted theorem proving
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candidate will develop practical learning-based systems for generative 3D/4D scene modelling, world models, and embodied intelligence. Key Responsibilities: Develop and implement machine-learning methods
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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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surface modelling; understanding of engineering drawing and design documentation practices; knowledge of dimensional chains, tolerances, fits, and geometrical tolerancing; ability to read and analyse
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the development of novel and innovative numerical techniques aiming at improving the integration between numerical simulations and geometric modelling and processing. Proof assistants such as Lean, together