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and digitizing archival data, strong knowledge of causal inference methods, good command of R and Python. Knowledge of machine learning methods is an asset. Strong command of English; command of either
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relevant to molecular and/or materials discovery, such as DFT, MD, and ML–based property prediction. Basic knowledge of physical chemistry, thermodynamics, or electrochemistry. Proficiency in Python and
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involving the analysis of omics or twin datasets. Essential qualifications include: A strong computational background, with experience in one or more programming languages (e.g. R, Python, Perl, or shell
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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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for hands-on experimental characterization techniques and data analysis. Skills in programming (e.g., Python, MATLAB) and simulation tools. Expertise in photonic integration is not a must, but having relevant
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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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-have: You can independently and confidently analyze quantitative data and you can write reproducible code (for example, in R or Python). Good-to-have: You have worked with large-scale text data, natural
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Advanced proficiency in Python and C programming languages You should also have good interpersonal and communication skills and should be able to work in a multi-cultural environment, both independently and
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process. You should also have: Competences in the field of mechanics, fluid-dynamics, electronics as well as coding (Python) are appreciated. Diversity, Equity and Inclusiveness ESA is an equal opportunity
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languages, for example Python, and general purpose deep learning frameworks, such as Tensorflow or PyTorch; The interest and ability to share knowledge with other ESA organisational units. You should also