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of aircraft with fundamentally different operational characteristics. You will simulate mixed-fleet operations in European airspace and analyze how they will impact the future air traffic system, as
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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 generative models for networks; analyze real network data from different application domains; design efficient algorithmic implementations of the theoretical models. You will be supervised by Dr
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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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behaviour that may be significantly different than what would happen in real-life bulk systems. For example, droplet confinement extends the upper range of critical capillary numbers where coalescence is
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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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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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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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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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. Building on these insights, you will run one dimensional mixed layer models to test how different conditions regulate stratification and mixing, and compare modeled responses with observations to expose