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environments Willingness to participate in underground field campaigns Skills in numerical modelling (e.g., fracture mechanics, fluid flow) Strong programming skills (e.g., Python, MATLAB, or similar) Experience
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numerical modelling tools Programming skills (Python and/or matlab required) Willingness to participate in underground field campaigns Ability to work in an interdisciplinary and collaborative environment
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, PorePy, or similar) Programming skills (Python, MATLAB, or similar) Experience with field data and/or underground experiments is an advantage Ability to work independently and lead research activities
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automation, data science, python. The ability to collaborate in a multidisciplinary research environment is essential. Personal initiative, ability to work systematically, reliability, responsibility, teamwork
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Master’s degree in Computer Science, AI, Machine Learning, Mathematics, Electrical Engineering, or a closely related field; or Master’s degree in Medicine (MD) with strong Python skills and some ML
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analysis, and experimental techniques Background in condensed matter physics or materials science Programming skills (C++, Python, ROOT) are a strong asset Good command of English (spoken and written
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techniques and the physical principles behind them Interest in scientific programming using Python Excellent communication skills and ability to integrate easily into interdisciplinary research team Additional
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Required Experience: PhD in Geodesy, Geomatics, Aerospace Engineering, Signal Processing, or a related field Proven experience in GNSS data analysis and processing Very good programming skills (e.g., Python
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sorting, calcium imaging, long-term tracking). • Experience with behavioral tracking and multimodal dataset integration. • Proficiency in Python, MATLAB, scientific computing, and pipeline development
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agricultural sciences or a related field Several years of research experience in field crop phenotyping Good statistical and programming skills (e.g. in R or Python) Evidence of research excellence through peer