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LES resolved wake dynamics and the coupling to aero-elastic models and wind turbine control. You will be a member of the Fluid and Offshore Mechanics section, which has a strong track record in offshore
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research projects will be considered.) Technical expertise in machine learning and model fine-tuning – 10% Demonstrated experience with neural network training, loss function design, embedding-based models
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quantum chemistry (DFT) and control engineering. PhD position in chemical reaction engineering (Kinetic modeling & thermal runaway) Supervisors: Sébastien Leveneur (sebastien.leveneur@ircelyon.univ-lyon1.fr
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NEST: https://nest-simulator.readthedocs.io Your tasks in detail: Work with the NEST main code base and experimental branches Dissect the spiking network simulation cycle into phases and capture the flow
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-learning–based segmentation, species classification and lineage tracking workflows for multi-species time-lapse data Optimise models and pipelines for real-time performance, enabling adaptive imaging and
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and the safety implications arising from these interacting processes. You will use and extend PyBaMM, which is an open-source Python-based battery modelling framework (https://pybamm.org
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be negotiated based on the selected candidate's availability. Group or Departmental Website: https://we3lab.stanford.edu/ (link is external) How to Submit Application Materials: Please apply via
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that support evidence-based decision-making, operational excellence, and continuous improvement across the department. With advanced expertise in healthcare data analytics and a deep understanding of academic
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Management Maintain and organize large, multi-modal imaging datasets (EUS, MRI, and associated clinical data). Ensure accurate data tracking, naming consistency, and version control across storage systems
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impacts of Iodine-131 releases, based on detailed knowledge of its physico-chemical processing in the atmosphere including the important inter-halogen reactions. Model findings will be cross-compared