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Empa within the framework of SURPASS are as follows: Volumetric prestressing of cast and 3D-printable SURPASS overlays using Fe-SMA fibres. Meso-scale finite element modelling of volumetric prestressing
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operational or technical problems and develop structured, quantitative approaches to solving them. • Strong understanding of mathematical modeling, optimization, decision analysis, simulation, or related
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simulation models, digital twins, data architectures and federated transport/manufacturing models. This role will provide the optimisation and decision-support capability required to analyse and improve
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Center for Drug Evaluation and Research (CDER) | Southern Md Facility, Maryland | United States | about 5 hours ago
, and Python, to develop mechanistic models to simulate human pharmacology and utilize High Performance Computing for data analysis. The goal of these quantitative systems pharmacology models is to define
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Reinforced Concrete (RC) substructures. Key Responsibilities: Simulate RC substructures under different boundary conditions. Design and fabricate test models. Develop algorithms for anomaly detections. Publish
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-particle physics, predictive modeling, and AI-enabled digital twins for magnetically confined fusion plasmas in tokamaks. The research will combine high-fidelity simulations, reduced transport modeling
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publication Expected Results The thesis should conclude in a rigid, validated simulation model of a coupled Reformer-SOEC-FTS process. The model should be able to handle a range of different operating
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: Utilize diverse teaching tools—including simulation models, anatomic specimens, and live animals—to train students in clinical skills across principal veterinary species (e.g., dogs, cats, horses
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candidate will develop practical learning-based systems for generative 3D/4D scene modelling, world models, and embodied intelligence. Key Responsibilities: Develop and implement machine-learning methods
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coding skills in Python with PyTorch, including reinforcement learning, imitation learning, and flow matching/diffusion-based generative models. • Experience designing hierarchical control