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ability to comfortable processing these data using tools like Seurat, Scanpy, or QuPath. Proficiency in Python or R, coupled with familiarity with machinelearning frameworks (e.g., scikit‑learn, PyTorch
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experience with Python Knowledge of energy systems analysis and modelling, AI and machine learning for data analysis. Experience with the modelling tool OSeMOSYS for energy and CLEWs application Awareness
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good understanding of all the main steps in crystallography-based structure solution is important Good programming knowledge, particularly in Python and/or C/C++ Ability to cooperate and work in a team
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include strong skills in GIS, large dataset handling, Python programming, and a record of scientific publications in the field. Excellent command of English, both spoken and written, is expected. Merits
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. change detection, semantic segmentation, or multimodal fusion, experience in deep learning for image analysis using time series satellite data, excellent programming skills (Python, PyTorch/TensorFlow
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and problem-solving skills are important, and previous experience or interest in coding (for example in R or Python) would be a clear advantage since the project involves handling and interpreting
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. Strong machine learning fundamentals (probability, statistics, optimization) and strong interest in time-series modeling and physics-guided machine learning. Proficiency in Python and modern deep learning
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++, maintained on github and configured via python. You will work with .root files and explore different event generators as well as machine learning tools and algorithms. The nature of LDMX as an international
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presentation of the result. The data analysis might include some coding in python What we offer A position at a leading technical university that generates knowledge and skills for a sustainable future. Engaged
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development. Qualifications Requirements for the position are: M.Sc. in engineering, neuroscience, statistics, quantitative biology, physics, mathematics or a related discipline. Fluency in Python programming