15 programming-"https:"-"https:"-"https:"-"https:"-"https:" "INSERM" PhD positions at AALTO UNIVERSITY
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and admission criteria at https://www.aalto.fi/en/study-options/aalto-doctoral-programme-in-electrical-engineering . Desired background We are looking for candidates with a strong background in one or
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, and decide reliably in complex, human–robot shared environments. The Mobile Robotics ( https://www.aalto.fi/en/department-of-electrical-engineering-and-automation/mobile-robotics ) and Intelligent
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, you will apply for the study right in doctoral studies at Aalto University School of Electrical Engineering. Thus, please see the student information and admission criteria at https://www.aalto.fi/en
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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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engineering, or a related field. Strong interest in doctoral research on audio deepfake detection, watermarking, source tracing, and generative AI. Good programming skills, preferably including Python and
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are described at https://kwantaekim.github.io/open-position/ Please note: Aalto University's employees should apply for the position via our internal HR system Workday (Internal Jobs) by using their
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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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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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Aalto University take approximately four years . You can find more information about Aalto’s doctoral program and its requirements here: https://www.aalto.fi/en/programmes/aalto-doctoral-programme-in
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researcher in the field of protein-nucleic acid hybrid materials We are seeking a doctoral researcher to join a Research Council of Finland-funded project ( https://research.fi/en/results/funding/88763