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Field
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to facilitate a rapid and efficient exchange among experimental and computational groups and Devise an approach in invertible predictive modelling that links semiconductor properties to the composition of lead
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the development of a low fidelity pump model that accounts for unstable and multi-phase flow behaviour through high fidelity simulations. This will be used to develop an integrated fuel system model that will
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, energy-related datasets. Proficiency in Python, MATLAB, and/or Julia for modeling, simulation, and data analysis. Familiarity with GIS tools (e.g. QGIS), time-series databases (e.g. InfluxDB), and version
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. Objectives: -Fabrication, characterization and modeling of the composite magnetic materials. -Modeling, fabrication, characterization and of the embedded power magnetic core inductors. -Implementation
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motivated and talented doctoral candidate to work on the biophysics of host-pathogen interactions using in vitro model systems mimicking chronic diseases. The project foresees ample collaborative
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will exploit multidisciplinary consortium expertise spanning design, modelling and simulation of photonic systems, sensor systems, signal processing and device manufacturing, development of machine
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resource-efficiency requirements. This collaborative doctoral project brings together the Institute of Advanced Simulation – Materials Data Science and Informatics (IAS-9) and the Institute of Energy
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apparatus equipped with thermocouples and thermal imaging to simulate realistic runaway events. Top-performing coatings will be validated in situ on live EV cells under controlled runaway conditions. Dr
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science, computer science, applied mathematics or a related field a strong background in machine learning, material modeling, and metals processing, modeling and simulation (This will be a clear advantage
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powerful, modern analytical techniques including chemical proteomics and metabolomics. They will have access to advanced synthesis facilities, as well as biological models, such as macrophages and organoids