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. It is expected that the data obtained will ultimately make it possible to the development of a new application-oriented model. We are looking for a strong postdoctoral fellow who will be able to become
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design recovery and stability strategies using large-scale simulation workflows. Build and expand realistic, continent-scale power system models (e.g., the European transmission grid). Implement and test
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to study grid stability, fault propagation, and recovery dynamics. Analyzing control and protection strategies using high-resolution time-domain models. Developing dynamic models for grid-forming and grid
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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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schools in the world. For more details, please view https://www.ntu.edu.sg/mae/research . We are seeking a Research Associate to develop a high-fidelity Artificial Intelligence (AI) framework for modeling
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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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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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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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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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dynamics, kinematics, acoustics/vibrations, fluid–structure interaction, control, or other mechanics-driven domains. Experience with applied computational methods and machine-learning–based modeling