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Instituto de Investigação e Inovação em Saúde da Universidade do Porto (i3S) | Portugal | about 1 month ago
formulations; - Availability to start working on the project on January 1st, 2026; - Fluent (writing and speaking) in English; Preferential: - Previous laboratory and research experience related to the project
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framework (e.g., OpenFOAM, ANSYS Fluent). Solid understanding of heat and mass transfer, turbulence modeling, and reaction kinetics. Experience with high-performance computing (MPI, parallel simulations) is
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doctoral degree (PhD. in Computer Science). Start date: Spring 2026. Project Description This PhD project explores the foundations of tractable probabilistic models, i.e. machine learning models that support
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Learning for Foundation Models’, where the aim is to adapt these models to new tasks without forgetting previous knowledge. The precise focus of the project can be defined in collaboration with
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. Under this job title, you will develop a CFD modelling platform to evaluate the combustion, flammable and toxic gases emission and dispersion characteristics resulted from battery fires/thermal runaway in
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Habitability and linked to the “Galactic Recipe for Exo-Planets” (GREP) project funded by the Research Council of Norway. GREP’s objective is to take exoplanet and exoplanetary system formation modelling
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the mechanisms by which these genetic events allow tumor cells to evade immunity and resist to various therapies. For this we use novel genome engineering approaches and in vivo models of cancer. Our team is
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dynamics, kinematics, acoustics/vibrations, fluid–structure interaction, control, or other mechanics-driven domains. Experience with applied computational methods and machine-learning–based modeling
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courses. DTU employs two working languages: Danish and English. You are expected to be fluent in at least one of these languages, and in time are expected to master both. Candidate Profile A PhD in chemical
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generally available after consultation and in line with upcoming tasks and (on-site) appointments FLEXIBILITY: Flexible working time models, including options close to full-time ( https://go.fzj.de/near-full