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for the project. Have documented programming experience in R, Python or other common programming languages. Have experience of quantitative analysis, computational modelling, bioinformatics, machine learning
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using R scripting, Python and machine learning. The successful candidate will be expected to participate in both laboratory and informatic research but the emphasis will depend on the student’s prior
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Knowledge of statistical methods in the context of biological systems Experience with programming (Python, Perl, C++, R) Well-developed collaborative skills We offer: The successful candidates will be hosted
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developments by the project team regarding advanced crystal plasticity models, different modeling strategies will be explored, including strain gradient plasticity [1,8,9], micromorphic approaches [2], and
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scale hydrological models The project offers the unique opportunity to connect novel processing and inversion techniques to experimental data from different regions and link the findings to relevant
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plasticity platform. Different machine learning strategies will be explored to capture the complex relationships between microstructural features and mechanical responses. In particular, the project will
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of space (mesosphere and lower thermosphere, MLT). The advertised position is primarily linked to the study of different metal atoms in the MLT and their relation to the re-entry of space debris. The project
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analysis is highly desirable. Experience with Earth observation or remote sensing data is a strong plus. Proficiency in programming in Python and experience with PyTorch, Scikit-Learn, or related modern
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, Engineering, mathematics or related disciplines with a strong background in data analysis, mathematical modeling and algorithms Good programming skills in Python/C/C++ Good oral and written skills in English
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mimicked with in vivo models of metastasis, which provides unique opportunities to mechanistically dissect what drives the different cell states. You will link clinically relevant phenotypes to putative