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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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more of the following areas: Stochastic processes, ideally including a background in ergodicity theory; Reinforcement learning, dynamic programming, and Markov decision processes; Programming skills
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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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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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with signal processing, time series analysis or machine learning Programming skills in MATLAB and/or Python What we offer The position is fixed term and follows Aalto's standard 2+2 model. It will be
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(but not required): experience with surface‑sensitive techniques, synthetic and preparative organic chemistry, lignin analytics, colloids, polymer processing, programming (e.g., Python/R/Matlab
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. Your experience and ambitions The position requires excellent programming skills in Python with demonstrable familiarity with PyTorch, experience in working on shared codebases, excellent applied math
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learning, particularly physics-informed or physics-constrained learning Programming and modelling skills (Matlab/Simulink, Python, C or other embedded-systems languages) Interest in embedded systems and
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doctoral researcher(PhD student) with a strong background in probabilistic machine learning, statistics, applied mathematics, computer science, or a related field, and strong programming skills, to
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strong interest in the following areas is highly desirable: Programming skills (MATLAB, Python, or C/C++) Real-time and instrumentation platform (dSpace, LabView) Microscopic measurements (X-Ray, EBSD