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, high-content imaging, data science and computational modelling are used to study the biomolecular networks governing cell fate specification and cellular behaviours, such as collective migration and
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project - “Towards efficient large model training via learning from small model”. He/She will be required to: (a) conduct research on knowledge distillation and transfer learning from small models
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inference (otherwise known as spectral retrieval), which involves using forward models in conjuction with Bayesian or machine learning-based techniques in order to derive posteriors on parameters of interest
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support ESA planetary protection function on its effort to modernise current approaches with the use of new statistical models to assess biological and chemical contamination risks. The Research Fellow in
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, high-content imaging, data science and computational modelling are used to study the biomolecular networks governing cell fate specification and cellular behaviours, such as collective migration and
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coordinated control of onboard power systems. Develop EMS strategies for power sharing, battery management, and voltage regulation. Deploy models on real-time HIL platforms (e.g., Typhoon HIL, OPAL-RT
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or more of the following: High-fidelity electrical system modelling using MATLAB/Simulink Electric vehicle, marine, or microgrid power system simulation Energy management system (EMS) or supervisory control
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: Multiphysics Modelling, Experimentation and Simulation (GeoLit)” financed by the Research Council of Norway. About the project/work tasks The significant presence of lithium (Li) in geothermal brines, combined
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the microgrid model and validate performance under different simulated conditions. Conduct testing of control strategies under various operating conditions such as extreme weather, load variation, equipment
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. Responsibilities include conceptualizing and implementing statistical and structural models, developing scalable algorithms for system optimization and control, conducting policy-relevant economic analysis