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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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coursework (examples): Computational physics/scientific computing and numerical methods for PDEs High‑performance computing (parallel distributed programming, GPU programming) Astrophysical fluid dynamics
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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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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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numerical/computational modelling methods (e.g. discrete element method or finite element analysis) Strong programming skills Good written and verbal communication skills in English Considered an
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: Extending full-spectrum non-gray models for gases to solid particles Experimental characterization of fuel particles using FTIR Mathematical model formulation and programming Model implementation in
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independently and in a team, and an interest in working with industry partners Willingness to do hands-on work underground and in the laboratory Basic programming ability in Python, MATLAB, R, or similar
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required): experience with surface‑sensitive techniques, synthetic and preparative organic chemistry,lignin analytics, colloids, polymer processing, programming (e.g., Python/R/Matlab), molecular