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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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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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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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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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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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R or Python). Good-to-have: You have experience working with large-scale text or visual data, or datasets related to history or culture. You tackle complex data challenges with curiosity and are
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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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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
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with clinical populations or translational research settings. Strong programming skills (e.g. Python, MATLAB, R) and experience with statistical modelling or machine learning. A strong publication record
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(e.g., Python, MATLAB, or similar). Excellent analytical, communication, and teamwork skills. A collaborative mindset and the ability to work in multidisciplinary and international teams. You are