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Field
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Experience with, or strong motivation to learn, programming (Python) and advanced data analysis methods Excellent oral and written communication skills in English Ability to communicate results clearly in
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statistical learning for high-dimensional data, solid programming skills (including R or Python) and a strong interest in interdisciplinary research with applications in molecular biology and cancer research
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languages (e.g. Python) Very good command of English (oral and written) Additonal skills and experiences that are advantagous: Experience in ocean dynamics Experience in ocean and/or biogeochemical modelling
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about shape optimization and the new opportunities offered by numerical design in turbomachinery. Prior experience in CFD and/or CFD code development (Python, C++, Fortran) would be a significant
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, Statistics or a related field and are motivated to apply advanced analytics to real-world digital health challenges. You have strong foundations in signal processing and are confident working in Python
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, geoinformatics, geoecology or related fields Programming skills (Julia, Python, MATLAB) and ability to work with time series and/or geospatial data Motivation to work at the interface of ML and environmental
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, Bioinformatics or related fields with an interest in biology or Biological sciences with a strong background in programming Expertise and experience in programming, Python or R Expertise to work in a Linux
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, numerical methods, or Geant4 / Monte Carlo simulations. Proven experience in scientific software development using C/C++, Python, MATLAB, CUDA, and/or other relevant programming tools. Demonstrated ability
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learning, data science, atmospheric sciences, geophysics, or related fields. Solid numerical modelling and programming skills (e.g., Python, TensorFlow, scikit learn) are essential, along with a basic
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methodological skills using, for example R or Python. - Strong collaborative skills and ability to take initiative. For further information about specific doctoral education, see, Study plans for doctoral studies