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Field
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methodology is essential. Demonstrated strong proficiency in programming (e.g., Python, PyTorch/JAX, or similar) and computational skills are required for this position. Candidates must have excellent
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background and research-oriented master thesis in a related field (e.g., signal processing, statistical machine learning, applied mathematics); Significant experience with programming (preferably Python). Good
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background and research-oriented master thesis in a related field (e.g., signal processing, statistical machine learning, applied mathematics); Significant experience with programming (preferably Python). Good
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engineering disciplines, including structural mechanics, hydrodynamics and machine learning Strong programming skills in Python and/or MATLAB Experience with scientific computing, CFD/FEM software, potential
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and/or hydraulic laboratories Experience with experimental research and data analysis Analytical and programming skills for scientific computing and data analysis (e.g. Python, MATLAB, R, or similar
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models, parameter estimation and model validation). Documented knowledge and experience in data analysis and scientific computing (such as proficiency in Python/R/MATLAB, data visualization, machine
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must have a solid background in mathematics or physics, and good programming skills (e.g. Python or MATLAB). You must meet the requirements for admission to the Faculty's PhD programme in Electronics and
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criteria Experience with laboratory development in power electronics would be desired. Scientific publications will be merited. Experience with programming tools (e.g., Python, Matlab, C/C++) will be merited
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collaboration with national and international stakeholders, the project aims to establish a validated, data-driven design framework. Key outcomes will include an open-source Python toolkit, measurable
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, linear algebra, probability theory, (Bayesian) statistics, optimization and elementary graph theory Familiar with machine learning and deep learning Programming experience (Python or Julia) and their