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methods (e.g., coding in Python/R, working with APIs, scraping data, building or applying models). • Can bridge theoretical insight with concrete technical implementation and empirical analysis. • Is
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of the following: A solid foundation in programming (particularly Python) and modern machine learning frameworks (e.g., PyTorch, TensorFlow). An interest in AI security, trustworthy ML, or the reliability of ML
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programming in Python and the modern AI ecosystem, including PyTorch, HuggingFace, and ONNX and you are comfortable working systematically with complex technical problems. Experience with industrial
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programming (particularly Python) and modern machine learning frameworks (e.g., PyTorch, TensorFlow). An interest in AI security, trustworthy ML, or the reliability of ML systems in safety-critical settings
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solid understanding of electromagnetic theory, microwave principles, and computational electromagnetics. You are comfortable working with full-wave simulation techniques such as finite-difference time
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-Mechanical System Design, or a related field Experience with modelling of mechanical and hydraulic systems Ability to develop and analyze dynamic system models using MATLAB/Simulink and Python or similar tools
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trajectory tracking, heading control, speed control, path following, disturbance rejection, adaptive control and control allocation for different propulsion layouts. The work will address the challenges
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this call will take on leading roles in the empirical work of the project, with different but complementary profiles. Both postdocs are expected to have an affinity to computational methods and tools
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quantitative analysis of data from experiments and other sources. This may include studying how blind screening and structured interviews affect the representation of different groups in selection processes
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frameworks. Further, it would be advantageous for applicants to demonstrate proficiency in at least one scientific programming environment such as Python, MATLAB, or R. A good understanding of hydrodynamic