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modelling and related research topics. Develop, implement and validate computational simulation models for impact and injury prediction using appropriate numerical methods and software tools. Design and
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and simulation: reduced-order modeling, simulation, data assimilation, digital twins, and computational methods for physical and engineering systems. Applications may include engineering, physics
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is available from 01 January 2027 or later. You can submit your application via the 'Apply' button above. Title PhD Position in Physical AI: Adaptive Foundation Models for Robotics Research area and
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sustainable manner. Key Responsibilities: Develop advanced experimental apparatus to investigate coupled multiphysics processes in porous materials (e.g., clay). Develop constitutive models and numerical tools
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expertise in discrete choice modelling, statistical modeling, traffic simulation, machine learning, and optimization techniques. • Strong expertise in discrete choice modelling, statistical modelling, and
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outstanding candidates whose research is centered on the mathematical modeling, analysis, numerical solution and simulation of physical, engineering, or complex systems. The position is particularly suited
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-Chem simulations for modeling aerosols and clouds in the Arctic region. - Analysis of the interactions between aerosols, clouds, and climate using data from the simulations. - Validation of the models
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, based on simulation models in Aspen, COMSOL, etc. c. Control theory & applications — including nonlinear systems, MPC, reinforcement learning, stability analysis, etc.
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energy systems and datasets. Validate and benchmark the developed models using building measurements, physics-based simulations and energy-system optimization models. Investigate how tabular foundation
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- “Suppressing multipath-induced false alarms for robust 5G mmWave UAV detection via geometric channel modeling”. Qualifications Applicants should have an honours degree or an equivalent qualification and research