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which factors determine feasibility and performance. What will you do? Improve, document and apply a regional groundwater model for variable-density flow groundwaterflow and coupled salinity simulations
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models for complex data, including temporal data. We are interested in both data-driven models as well as models built from synthetic data. Within privacy, we are interested in different types of privacy
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. Interaction with the different partners of the project will help to properly define the input data (source model, environment properties), and interpret the output data (pressure, displacement, velocity
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collide, condense and evaporate. Guided by the results from the field campaigns, a new Microphysics Informed Large-Eddy-Simulation (MiLES) model will be developed. The tested and verified MiLES will be
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focuses on the development of GPU-accelerated, high-fidelity thermal runaway simulation models for lithium-ion battery cells, modules, packs, and complete battery systems. Thermal runaway is a chain
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. Experience and expertise in human mobility simulation and prediction with agent-based modeling and deep learning techniques. Proficient in Python programming for geospatial data processing, modeling, and
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development, spin simulations, or MR signal modeling is highly desirable. Strong programming skills, for example in Python, MATLAB, C/C++, or a comparable scientific computing environment. Experience with
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). The successful candidate will contribute to the modeling, simulation, and co-design of next-generation Quantum-HPC (QHPC) architectures, with particular emphasis on integration of quantum and HPC distributed
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research themes combining theoretical analysis, probability, and computational modelling. Core duties include: Researching the non-local geometry and topology of Gaussian random fields, and random Laplace
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the same genetic material generate markedly different cells, and how this information is encoded within the genome. We utilize stem cells and cancer model systems, though projects range across disciplines