37 modeling-and-simulation-"UNIVERSITY-OF-SOUTHAMPTON" Postdoctoral positions in United Arab Emirates
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systems (ITS). In particular, the successful candidate will conduct cutting-edge research in: Developing physics-informed neural networks (PINNs) for complex dynamical systems modeling and observer design
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modeling in smart mobility systems Required Qualifications: The requirements for the applicants include: Ph.D. in Electrical Engineering, Computer Science, Operations Research, Applied Mathematics
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Reality to elicit users’ preferences for innovative transport systems. Applicants with a background in behavioral analysis and mathematical modelling are encouraged to apply. Terms of employment include
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. The researcher is expected to have (i) strong machine learning skills to improve model performance and robustness, and (ii) exemplary passion and motivation to pursue multidisciplinary research at the intersection
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the safety and robustness of human-robot interaction. The postdoc will work on projects focusing on one or more of the following: Learning-Based Control: Developing reinforcement learning, imitation learning
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the frontiers of developmental biology and disease modeling. The laboratory integrates stem-cell biology, fluorescence imaging, bioinformatics, and advanced nano- and micro-engineering to decode organogenesis and
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, Neuroscience, or a related field. A strong background in functional neuroimaging with experience in decoding and/or encoding models is required. Candidates with experience with recurrent neural networks will be
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for physical systems. The postdoc will work on projects focusing on one or more of the following: Robot Learning & Autonomy – Developing algorithms that allow robots to learn (via exploration or imitation) from
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or Python) is required. Experience with large-scale concrete testing, modeling, and simulation (e.g., COMSOL, Abaqus) is a plus. Familiarity with AC impedance spectroscopy, electro-mechanical impedance, wave
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, climate, and human health. Examples of current active projects include: Developing optimization models to analyze and mitigate fine particulate matter (PM2.5) exposure from various infrastructure systems