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shaping will be central to the study. The numerical model will be based on the boundary element method (BEM) and semi-analytical approaches developed at I2M. The experimental proof-of-concept will leverage
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-based models that optimise and control pharmaceutical manufacturing processes effectively. Your main responsibility is to develop and enhance discrete element models (DEM), integrating physics-based
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-based techniques (e.g., deep neural networks) will be used to automatically learn the system dynamics and the modelling errors, as well as to obtain an automatic tuning of the cost parameters/constraints
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behaviours of multi-agent systems in response to changing internal states and external environmental conditions. Both traditional model-based approaches and modern learning-based control techniques will be
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theory, modeling, and AI-assisted optimization activities within the consortium. Reporting and dissemination of the results. Share this opening! Use the following URL: https://jobs.icfo.eu/?detail=1074
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computing infrastructure. The main tasks will include: designing, implementing and documenting programming components supporting experiments with AI models (including LLM, generative models, complex system
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of Systems and Control, Department of Electrical Engineering, focused on innovative research in Data-driven Modelling and Optimisation for Fast Charging of Lithium-Ion Batteries. About us At the department
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distribution modelling • Track record of peer-reviewed scientific publications • Experience in ship-based sampling campaigns • Ability to work independently as well as collaboratively in
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will focus on the development of advanced thermoelectric cooler designs for on-chip cooling applications within the MGICIAN Doctoral Network. The project will address model-based and data-driven design
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PhD Studentship: Preclinical modelling and therapeutic targeting of glioblastoma infiltrative margin
PhD Studentship Advert Title: Preclinical modelling and therapeutic targeting of glioblastoma infiltrative margin Supervisors: Prof Ruman Rahman, Dr Stuart Smith, Dr Phoebe McCrorie Project Overview