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Proficiency in at least one programming language, preferably Python; experience with scientific computing, numerical modeling, or machine-learning frameworks is an asset Strong analytical skills with a solid
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numerical simulations to reproduce and predict magnetically confined fusion plasma experiments 2.Development of transport models based on simulation data and their implementation into integrated transport
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evaluation Knowledge of numerical simulation, for example with land surface or hydrological models Genuine interest in data science and earth sciences Good organizational skills and ability to work both
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modelling and improving Earth System Modeling by better merging of measurement data and model simulations. This PhD project focuses on improving how we estimate key parameters in land-surface and ecosystem
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, molecular simulations, and their application to bioprocesses and bioseparations Proficiency in at least one programming language, preferably Python; experience with scientific computing, numerical modeling
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Your Job: We are looking for a PhD student to contribute to the development of fast, accurate, and physics-informed machine learning models for predicting blood flow in patient-specific vascular
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AI-powered digital modelling. The project aims to deliver battery-free micro‑power solutions for smart, climate-responsive urban infrastructure. The Research Assistant will play a central role in
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services. The innovative application of machine learning to exploit satellite data within numerical modelling links to other scientific applications beyond those of the atmosphere. The project builds
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, accurate, and physics-informed machine learning models for predicting blood flow in patient-specific vascular geometries. Current simulation-based approaches require complex 3D meshes and are often too slow
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: Investigation of precipitation and crystallization processes in phosphorus recovery using experimental and numerical methods Supervisor: Prof. Sergiy Antonyuk C2: Experimental investigation and modeling