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mindset and an interest in future mobility systems. Required qualifications include: A master’s degree (with at least distinction) in engineering, computer science, physics, applied mathematics, or a
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/ Postdoctoral position in simulation-based inference for particle physics Uppsala University, Disciplinary Domain of Science and Technology, Faculty of Mathematics and Computer Science, Department of
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traffic-simulation platforms such as dSPACE, CARLA, SUMO, or comparable tools. Experience applying machine learning, computer vision, time-series analysis, generative AI, or sensor fusion to vehicle
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software to include 3D anatomic features as well as features representing regional function; developing and testing methods for regional blood flow and ventilation; and utilizing output of quantitative
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Electrical Engineering or Computer Science or related fields. Solid background in power systems dynamics and modeling including load flow, short-circuit, and contingency analysis. Experience with power
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workflows for grid modeling, optimal power flow (OPF), surrogate modeling, and data-driven analysis of large-scale electric power system simulations on DOE leadership-class computing resources. The
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Measurement and analysis of tissue microstructure, such as diffusion imaging Bloch- and EPG-based physics simulation Multi-modal quantitative MRI MRI experiment and analysis, particularly in neuro imaging
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neural networks, and physics-informed machine learning in power systems Set up benchmark grids and generate or process suitable simulation data for power-flow, voltage-prediction, or state-estimation
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thermal and aerodynamic environments integrating inputs from the research team, providing validation data for analysis as well as correction factors accounting for unmodeled physics. The MDAO (Multi
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, PCR machines for gene amplification and cloning, and flow cytometers and cell sorters for single-cell analysis and isolation. Collaborates with internal and external students, scientists, researchers