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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
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programming and software development. Familiarity with Python and statistical computing libraries, like PyTorch or JAX, etc., would be preferred. You are a motivational teacher, with an encouraging teaching
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: Experience with data management tools (Excel, CSV, SQL, R, Python) Preferably has experience in clinical research data management. Salary The salary will be in scale 10 (max. 5,669 Euro per month, depending
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: Ability and motivation to conduct fieldwork in alpine environments, and willingness to obtain a UAV license. Interest in coding (e.g., Python, Matlab, R) and numerical modelling. Enthusiasms for working
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PhD Candidate on The Future of Mixed Methods Research /Junior Lecturer in Methodology and Statistics
are appreciated but not required. Good research skills evidenced by good grades for research methods courses. Excellent data analytical skills, as evidenced by a good command of R and/or Python Experience with
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; experience in either is a plus, not a must. Strong motivation to advance data analysis or modeling is essential. Proficiency in scientific programming (e.g., Python, Fortran), version control, and reproducible
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environmental datasets; proficiency with Python, MATLAB, or similar scientific programming environments. Ability to work with large datasets, develop reproducible workflows, and apply modern data science tools