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cognitive tasks (Python/MATLAB) Apply computational models of behavior and cognition Analyze high-dimensional datasets (behavioral, physiological, neuroimaging) Publish in leading international journals and
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, retrieval products) Strong programming skills in Python Experience in machine learning, ideally including deep learning architectures such as graph neural networks, transformers, or spatio-temporal models
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university Background or strong interest in mechatronics, optics, electronics with hands-on experience Manual dexterity and careful working style, workshop and/or electronics experience is a plus Basic python
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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
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agricultural, data or engineering sciences, or a closely related field Experience with fieldwork Good statistical skills Programming experience (e.g. in R or Python) Good standard of written and spoken English
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at international conferences Profile Education: An MSc degree in Geophysics, Physics, Mathematics, Data Science, or a related quantitative field Technical Skills: Strong programming proficiency (primarily Python
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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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(e.g., in R or Python) A good standard of written and spoken English as well as a driver’s license are mandatory Good writing skills and the ability to work in a team with an interdisciplinary background