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Field
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environment Programmers comfortable processing and analyzing large data sets in Python; experience with C++ (or another low-level language) is a plus Strategic thinkers with demonstrated interests in strategic
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environment Programmers comfortable processing and analyzing large data sets in Python; experience with C++ (or another low-level language) is a plus Strategic thinkers with demonstrated interests in strategic
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comfortable processing and analyzing large data sets in Python; experience with C++ (or another low-level language) is a plus Strategic thinkers with demonstrated interests in strategic games and/or competitive
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, fluid-structure interaction) Desire to develop interdisciplinary expertise across hydrodynamics and structural mechanics. Experience with or willingness to learn: Programming (e.g. C++, Python, Matlab
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are expected. Knowledge in parallel programming is desirable. Prior knowledge in differential-algebraic equations, Gaussian processes or kernel based methods is a plus; programming experience in Python or C/C
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of challenges of building large-scale systems. Programming skills in Python. A good Bachelor’s Hons degree (2.1 or above or international equivalent) and/or Master’s degree in a relevant subject (physics
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understanding of challenges of building large-scale systems. Programming skills in Python. A good Bachelor’s Hons degree (2.1 or above or international equivalent) and/or Master’s degree in a relevant subject
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proof-of-concept software tools Machine learning is a plus Strong analytical and programming skills are required (Python, Matlab, and C/C++). Prior proven experience in data-driven innovation projects is
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languages such as Python for data analysis and simulations. Experience with optical systems, astronomical observations, or satellite tracking would be advantageous, although not compulsory. Strong analytical
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external enrolment procedures. Selection criteria Demonstrated experience in programming and system development. Expertise in Python programming and data analysis. Experience developing Machine Learning