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relevant discipline. Experience with modelling or simulation tools (e.g., MATLAB/Simulink, PSCAD, PowerFactory, or Python) is desirable.
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candidate will also bring experience in 3D printer customization, software/hardware integration, and CAD modeling skills. Responsibilities Minimum Qualifications Preferences Type Benefited Staff Special
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. Special emphasis is given to numerical simulation to reduce the required experimental effort, but the validation of the models on laboratory setup will remain an essential asset. Your role The PhD-student
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SD-26106-PHD POSITION ON ASSESSMENT AND MONITORING OF SOIL HEALTH AT CONTAMINATED SITES USING GEO...
the existing NBenefit$ tool of LIST by combining system dynamic modelling of ecological processes, life cycle assessment (LCA), and life cycle costing (LCC) into an evidence-based simulation framework
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position We are seeking a motivated doctoral student to work on biomethane system scenario design and assessment. The research studies include modelling and simulation from feedstock supply and thermal
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, such as model evaluation and data pipelines, and their applied forms, including training, fine-tuning, and deployment of models using frameworks such as PyTorch or TensorFlow. Solid proficiency in Python
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PhD in Optical Characterization and Photonic Performance Analysis of Liquid Crystal Polymer Coatings
under external stimuli. This PhD project will focus on the advanced optical characterization, modelling-supported interpretation, and performance benchmarking of these coatings. You will quantify how
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and ML models support the coordinated operation of DERs and smart buildings in the context of limited data? How can AI and ML models increase distribution systems observability via surrogate models
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of multipoint in situ measurements and numerical simulations performed with the ion-kinetic Vlasiator code to quantify how interplanetary shocks modify solar wind current sheets across a range of
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or noisy observations. Addressing these uncertainties typically relies on ensemble-based simulations using computationally expensive high-fidelity models. This PhD project addresses this challenge by