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Field
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://www.esa.int/ Field(s) of activity/research for the traineeship This fellowship aims to advance the development and application of geospatial foundation models tailored to multimodal and multiscale EO datasets
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citizen experience, simulation data, and fairness evaluation. You will: Design and test VR environments that represent alternative configurations of urban space (e.g. street reallocation, mobility hubs
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, focused on surrogate modeling. The goal of SmartEM is to develop surrogate models, or systems of surrogates, for complex industrial systems or their high-fidelity simulations. The European project will
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and energy systems, you will combine transport simulation models (e.g., SUMO) with power grid tools (e.g., OpenDSS) to build a co-simulation environment based on real-world data. The outcomes of your
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. Demonstrable experience in developing analytical and numerical models for dynamic systems (e.g. FEM, multiphysics simulations, etc.). Strong critical thinking, problem-solving, and inventive skills, with
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compact models. Compact models are the optimum trade-off between the required physical functionality and computation intensity. As such, they enable the simulation of advanced and high-integration-density
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, while also developing custom Python-based simulation scripts. TU Delft will support your research with access to the DelftBlue supercomputer for large-scale and intensive modelling. In the second phase
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techniques with the constraints of silicon processes. In parallel, you will develop on-chip antenna structures, performing full-wave EM simulations and translating these designs into manufacturable layouts
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simulations and translating these designs into manufacturable layouts. Your work bridges circuit design, electromagnetic modeling, quasi-optical system analysis and hands-on validation. A key part of your role
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on diffusion and/or flow matching, that can rapidly adapt pre-trained models to new tasks and select optimally informative simulations at each stage in a design process. Collaborate closely with PhD students