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optimization techniques. You have experience with modern Deep Learning Frameworks (PyTorch, Tensorflow, Jax) and proven ability of CUDA and Python programming. Knowledge of, or prior experience with, optimizing
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opportunities offered by cutting-edge CFD methods. Prior experience in CFD and/or CFD code development (e.g., Python, Fortran, C++) would be a considered as a strong asset. Selection process The recruitment
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chemistry, bioinformatics, or a related field §you have mastered molecular modeling techniques, machine learning algorithms, and programming languages like Python §you are highly collaborative, with excellent
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of X-ray methods Knowledge of X-ray optics Knowledge of synchrotron science Knowledge of catalysis and energy storage Experience with programming languages (ideally Python), SPS controls system Fluent in
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programming and data analysis (e.g. Python, Julia, MATLAB, C/C++, or similar) • Ability to work independently and as part of a collaborative research team Desirable skills / experience
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. Skills in numerical tools and programming are desirable (MATLAB, python, C++ etc). Any experiences with engineering design, structural/aerodynamic/aeroelasticity modelling, manufacturing/assembly process
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technologyEducation LevelPhD or equivalent Skills/Qualifications Must-haves: Education: Master’s degree in Telecommunications, Computer Science, AI, or a related engineering field. Technical Skills: Strong Python
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Proven laboratory experience and programming proficiency (Python or a comparable language) Ability to work independently and self-reliantly, while maintaining strong teamwork and collaboration skills
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, programming (Python) and advanced data analysis methods Excellent oral and written communication skills in English Ability to communicate results clearly in presentations and in writing Self-motivated and well
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Science, or a closely related field Prior coursework and working experience in data science, machine learning, statistics, or related areas Proficiency in Python for data analysis and modeling, machine learning