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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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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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Technology, KTH Royal Institute of Technology and TUL Montanuniversität Leoben, so your work will be discussed both in the scientific community and with the people planning Finland’s hydrogen
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Institute of Informatics in Japan, Prof. Xavier Serra at Universitat Pompeu Fabra in Spain, Prof. Gustav Eje Henter at KTH Royal Institute of Technology in Sweden. The collaboration network has a
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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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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