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Foundations: Knowledge of optimization techniques (e.g., LP, CVX, etc), including for/with ML (first order methods, data-driven algorithms, etc). Data Foundations: Hands on experience in data analysis (Python
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probabilistic modeling Good programming skills in Python/C/C++ Good oral and very good written skills in English Enjoys working in an international and inter-disciplinary research group TU Delft (Delft University
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requirements Required: MSc degree (or equivalent) in Aerospace Engineering, Air Transport, Operations Research, or related field Strong analytical and modeling skills Proficiency in programming (such as Python
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, Engineering, mathematics or related disciplines with a strong background in data analysis, mathematical modeling and algorithms Good programming skills in Python/C/C++ Good oral and written skills in English
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-of-the-art techniques. Image/data processing skills in Python or Matlab. Proficiency in written and spoken English. Doing a PhD at TU Delft requires English proficiency at a certain level to ensure
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design and digital signal processing. Hands-on RTL design skills (SystemVerilog / Verilog / VHDL) plus scripting (Python / MATLAB / C/C++). Strong command of English. Strong team player with excellent
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in Python and affinity with large geospatial datasets. Interest in interdisciplinary research at the interface of geoscience, engineering, and societal impact. Good communication skills and willingness
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skills (Python) and knowledge of deep-learning frameworks (PyTorch) are expected. A certain affinity towards turning complex concepts into real-world practice is desired. The successful candidate is
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Aerospace Engineering, Aeronautics or a comparable degree, thorough knowledge of AI/ML methods, acoustics, and air traffic management are preferred, as well as excellent programming (Python, Java, C++, …) and
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; experience in either is a plus, not a must. Strong motivation to advance data analysis or modeling is essential. Proficiency in scientific programming (e.g., Python, Fortran), version control, and reproducible