152 programming-"https:"-"Inserm"-"FEMTO-ST" "https:" "https:" "https:" "https:" "https:" "P" "Dr" positions at ETH Zurich
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, especially with an interest in AI and judicial processes Computer Science or Data Science, especially with practical programming experience Currently enrolled as a student at a Swiss university We are also
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stimulation techniques. The goal is to develop safe, efficient, and environmentally acceptable stimulation strategies for geothermal applications across Europe. Job description Plan, conduct and analyse
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processes underlying induced seismicity Plan and conduct controlled production experiments at the BedrettoLab to test hypotheses and validate models Translate modelling results into simplified and scalable
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you will have the opportunity to contribute to teaching activities within the group. The position is funded for 4 years, at 100% employment. The project will be supervised by Dr. Céline Labouesse
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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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received by 1 November 2025. Note: as an option, one of the four reference letters can be about teaching. For more details, please see the website: https://math.ethz.ch/fim/postdocs.html Application
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, programming, and operation of DIW equipment Determination of optimal debinding and sintering steps Characterization of the sintered materials: density, microstructure, defects, shrinkage, possible distortions
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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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related field with experience in water chemistry, silviculture, experimental field work and large-scale data analysis. You must have good statistical skills and programming experience (e.g., in R or Python
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Strong programming skills in Python (e.g., PyTorch) Experience with data processing, visualization, and experimentation workflows Knowledge of additive manufacturing processes or industrial monitoring