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engineering and information sciences. In parallel with basic research, we develop ideas and technologies further into innovations and services. We are experts in systems science; we develop integrated
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, computational science, or closely related). Relevant coursework (examples): Computational physics/scientific computing and numerical methods for PDEs High‑performance computing (parallel distributed
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engineering and information sciences. In parallel with basic research, we develop ideas and technologies further into innovations and services. We are experts in systems science; we develop integrated
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is one of the six schools of Aalto University. Our portfolio covers fields from natural sciences to engineering and information sciences. In parallel with basic research, we develop ideas and
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atomistic simulations. This project requires creative thinking and programming, as well as technical expertise in materials simulations, electron–phonon physics, and machine learning. We further appreciate
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. A strong background in energy-system modelling, mathematical optimization, machine learning, reinforcement learning, or data-driven decision-making is appreciated. Experience with programming and
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candidates with a strong background in one or more of the following areas: Stochastic processes, ideally including a background in ergodicity theory; Reinforcement learning, dynamic programming, and Markov
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environment cleaning, combining and organising data into analysis-ready datasets carrying out statistical analyses and modelling using the R programming language conducting cost, benefit and effectiveness
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research experience in mechanics Prior exposure to numerical/computational modelling methods (e.g. discrete element method or finite element analysis) Strong programming skills Good written and verbal
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Excellent programming skills and experience with C Interest in computational discrete mathematics and algorithmic experimentation NOTE! This position is open only for Aalto University students. What we