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Field
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engineers learn together. You’ll build a shared foundation in quantitative trading, programming, data analysis, mathematical modeling, and modern machine learning and AI tools before applying those
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learning research, possessing a combination of mathematical maturity and advanced engineering skills: Education: A PhD in Computer Science, Mathematics, Statistics, or Theoretical Physics at the time of
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degree at an earlier date may also be considered if there are special grounds, for example, different types of statutory leave of absence. The research tasks require a solid mathematical background with
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absence. The research tasks require a solid mathematical background with proven advanced experimental capabilities and excellent programming skills (e.g. C++, ROS, Matlab, etc.). You should have a
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mathematical and analytical skills; strong programming experience in C++ and Python; experience with ROS or related robotic software frameworks; the ability to conduct high-quality independent research while
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background in numerical methods, mathematical modeling, and network simulation or analysis Good understanding of power systems, gas networks, or coupled multi-energy systems Proficient in programming
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, applied mathematics, computer science, computational engineering or a related discipline Sound knowledge of mathematical optimisation, control engineering or energy-management methods Experience in
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modelling Experience in scientific programming and numerical simulation using MATLAB and/or Python, as well as in processing and analysing experimental data Practical experience with laboratory battery
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/ doctoral school). This is part of your personality: Completed master or diploma in the atmospheric sciences (e.g., meteorology, climatology), or a related field; Strong programming skills; Data analysis
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candidates with a strong background in one or more of the following areas: Stochastic processes, ideally including a background in ergodicity theory; Reinforcement learning, dynamic programming, and Markov