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to exoplanet discovery and space‑weather environments. You will work in Aalto’s High‑Performance Computing Lab within the PEAS (Population of Exoplanets around Active Suns) consortium alongside leading teams in
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performance analyses for backscatter-assisted non-line-of-sight positioning Applying detection and estimation theory to questions such as detectability, identifiability, positioning accuracy, and robustness
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uncertain sensing, with emphasis on safe operation among people, socially aware perception and behaviour prediction, and semantic/dynamic environment understanding. Our research environment offers close
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different specific requirements, but we are generally interested in candidates with following merits: Track record of scientific publications; Prior experience with high-performance computing (HPC) systems
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demonstrated interest in numerical methods, and high-performance computing. Applicants should hold a master’s degree with excellent academic performance in Mechanical Engineering, Technical Physics, or a closely
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from 50 countries, it is the largest department at Aalto University and the leading computer science research unit in northern Europe. Computer science research at Aalto University ranks high in several
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legislation, as we aim to achieve zero-emission buildings and increase the energy flexibility of buildings. In the post-pandemic period, we have also learnt that high-performing ventilation is critical
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by the Multi-energy System Planning and Operation Research Group at Aalto University School of Electrical Engineering . The research will focus on the modelling, optimization, and operation of AI
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are essential. Knowledge of fire simulations as a plus. Skills of performing non-linear numerical analyses using general-purpose finite element analysis software particularly Abaqus are essential. Skills
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an advantage. Applicants must fulfil the eligibility and admission criteria for Aalto’s Doctoral Programme in Engineering as specified at Aalto Doctoral Programme in Engineering | Aalto University . Your network