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Field
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algorithms for battery management systems (BMS) in electric mobility and micro mobility applications. The primary focus will be on creating and optimizing state-of-charge (SOC) and state-of-health (SOH
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interaction and/or surface flux computation, including familiarity with bulk flux algorithms and observational QA/QC procedures. Experience with processing, analyzing, and interpreting multi sensor
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, through digital participation platforms, algorithms for identifying safe and sustainable routes, multimodal mobility hubs, as well as interactive simulation and visualisation tools for designing urban
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An already acquired Phd in Electrical Engineering, Computer Science, Applied Mathematics, or a relevant field Affinity for formal and simulation models, as well as algorithmic solutions to problems
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processes and stochastic analysis Theoretical analysis of neural networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and algorithmic perspectives on large
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. Ability to bridge technical and pedagogical perspectives, translating between educational needs and algorithmic design. Excellent communication and academic writing skills. Preferred Attributes Experience
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members. Participate in traffic scenario generation project and pedestrian modeling project. Develop sophisticated AI-driven algorithms that create realistic, safety-critical test scenarios for autonomous
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by working to develop novel algorithms on finite element method, isogeometric analysis, geometric modeling, machine learning and digital twins to study various applications such as computational
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. The current position deals with free-space optical transmission experiments. It will involve challeges with low SNR, atmospheric turbulence issues, coherent recevers and related signal processing algorithms
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to the application deadline Experience from sea ice field work or polar expeditions Experience in work with oceanographic or meteorological data and models What you will do Apply, validate and improve algorithms