157 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "U.S" positions at Forschungszentrum Jülich
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the application which project you are specifically interested in. Further details on the projects can be found here: https://www.fz-juelich.de/en/jcns/careers/fellowships/tasso-springer-fellowship-program Your
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English (at least B2 level according to the CEFR: https://go.fzj.de/languagerequirements ), ideally supported by a certificate confirming the language level. Knowledge of German is not prerequisite
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, as well as enjoyment of cooperative collaboration You have a very good command of written and spoken English (at least B2 level according to the CEFR: https://go.fzj.de/languagerequirements ) Our
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hierarchies You have a very good command of written and spoken English (at least B2 level according to the CEFR: https://go.fzj.de/languagerequirements ), ideally supported by a certificate confirming the
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user of supercomputers and sufficient programming skills You have a very good command of written and spoken English (at least B2 level according to the CEFR: https://go.fzj.de/languagerequirements
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30 days of vacation plus additional days off (e.g. between Christmas and New Year`s) FLEXIBILITY: Flexible working time models, including options close to full-time ( https://go.fzj.de/near-full-time
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, engineering, physics, biophysics, applied mathematics, computational biology or a related quantitative field Strong background in deep learning for image analysis / computer vision, ideally on microscopy time
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Please feel free to apply for the position even if you do not have all the required skills and knowledge. We may be able to teach you missing skills during your induction. Our Offer: We work on the very
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for building a career in academia or industry Professional development through JuDocS, including training courses, networking, and structured continuing education ( https://www.fz-juelich.de/en/judocs
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the INW-1 machine learning team on data handling, online analysis, design of experiments (DoE), and data categorization to enable efficient and automated evaluation of operando experiments Collaboration