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(FSTM) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering, Computer Science, Life Sciences and Medicine. Through its dual mission
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) assess future changes in these patterns under different global warming scenarios. Requirements: The successful applicant should hold a MSc or PhD degree in physics, mathematics/statistics, climate science
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Systems Engineering, Computer Science, Software Engineering, Mathematics, or a related field Experience with interdisciplinary, multidisciplinary, or transdisciplinary research projects and related research
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PhD or equivalent qualification in computer science, statistics, mathematics, physics, and/or engineering, or a degree in biological science with demonstrated experience in computational and statistical
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: completed scientific higher education degree (PhD) in the field(s) of Earth system science, physics, climate physics, geosciences, mathematics, computer science or a comparable field demonstrated experience
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Human Technopole (HT) is an interdisciplinary life science research institute, created and supported by the Italian Government, with the aim of developing innovative strategies to improve human health. HT is composed of five Centres: Neurogenomics, Computational Biology, Structural Biology,...
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of robotics Solid mathematical foundation paired with practical robotics experience Strong programming skills in (at least one of) Python/C++ Familiarity with robotics frameworks like ROS; optimization-based
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(FSTM) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering, Computer Science, Life Sciences and Medicine. Through its dual mission
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, oceanography, physics or mathematics, with a strong interest in the application of statistical and data analysis methods #excellent knowledge in UNIX/Linux and Unix-Scripting #very good knowledge in programming
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, Computational Biology, Applied Mathematics or related field Interest in interdisciplinary research Applied experience with machine/deep learning methods Ability to handle multiple projects in a dynamic