50 coding-"https:"-"Prof"-"FEMTO-ST" "https:" "https:" "https:" "https:" "https:" "https:" "P" "Dr" positions at ETH Zurich
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of today's knowledge in laser material processing. Curious? So are we. Please send your full application (cover letter, transcripts, CV, references)with reference ”TC-LA” to Dr. Timo Schudeleit, Head of Laser
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will not be considered. Further information about the RELab and optohive can be found on our website or Linkedin . Questions regarding the position should be directed to Dr. Dominik Wyser, dominik.wyser
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100%, Zurich, fixed-term Dr Daniel A. Richards and Professor Andrew J. deMello, in the Institute for Chemical and Bioengineeringat ETH Zürich (ETHZ), and Dr Loïc Burr of the Swiss Center
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possible. Further information about the Atmospheric Physics group can be found on our website . For any questions related to the project or this position, please do not hesitate to contact Dr. Jan
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about the ETH AI Center can be found on our website . Questions regarding the position should be directed to Dr. Manuel Kaufmann, manuel.kaufmann@ai.ethz.ch (no applications). Pre-selection is carried
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and training machine and deep learning models using Keras/TensorFlow and/or PyTorch, supported by experience in statistical data analysis. You have experience with collaborative coding practices
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Instruction and the research lab here . You can also contact Professor Dr. Martina Rau (martina.rau@gess.ethz.ch ) with questions about the position. Please submit the following application documents using
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information about ETH Zurich can be found on our website . Questions regarding the position should be directed to Dr. Florian Lienhard email: lflorian@ethz.ch (no applications). Please note that we exclusively
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for an interview by 31.03.2026. Start date will ideally be June-July 2026. Questions regarding the position should be directed to Dr. Martin Hartmann, martin.hartmann@usys.ethz.ch . Please note that we exclusively
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community Contribute to R data publication packages washr and fairenough Profile You care about data and code being concise and easily reusable You know how to use standard data science tools (Git, GitHub, R