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collaboration with Prof. Giovanna Tinetti and her team and collaborators at KCL. The main purpose of this role is to develop new and/or to use existing models to simulate the atmospheres of exoplanets and use
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, Bioimaging Sciences Position Description: Join an exciting effort to develop a low-field, low-cost, MRI scanner for screening mammography. You will participate in the development of MRI reconstruction
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motivated Post-Doctoral Associate to join our team with a strong background in robot control, machine learning, and differential geometry to work on the development of advanced algorithms to enhance
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. The overall aim of this project is to address these challenges by: Developing new data-driven and physics-based models of battery behaviour. Designing advanced BMS algorithms for real-time monitoring and
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. This role will involve the development of novel lattice QCD algorithms and high-performance computing (HPC) codes, and/or exploring applications of artificial intelligence (AI) to lattice simulations
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research in neuro-symbolic AI, with a focus on using generative AI and prompt engineering as a method to engineer knowledge graphs one can trust. This includes the design of algorithms and architectures, but
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dynamics, solid mechanics, soft matter or active matter. • To become familiar with simulation algorithms as needed, assist in the development of new ones, test and document any newly developed
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techniques for integrating such solutions into modern SDV middleware. Responsibilities: Conduct research in runtime analysis and reconfiguration of in-vehicle TSN networks. Develop algorithms and prototypes
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generation data-driven stochastic and distributionally robust optimization methodologies or (ii) develop advanced fairness promoting stochastic optimization frameworks. In coordination with Prof. Shehadeh
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research in neuro-symbolic AI, with a focus on using generative AI and prompt engineering as a method to engineer knowledge graphs one can trust. This includes the design of algorithms and architectures, but