162 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Mines Paris PSL" positions at Forschungszentrum Jülich in Germany
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experience with NMR and wet-lab chemistry Willing to work within a multidisciplinary and collaborative research environment Curiosity and excitement to learn Good knowledge of spoken English Our Offer: We work
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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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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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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 latest issues that impact our society
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thesis will be appropriately remunerated by us In addition to exciting tasks and a collegial working environment, we offer you much more: https://go.fzj.de/benefits We welcome applications from people with
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found on the BMI website: https://go.fzj.de/bmi.tvoed . The monthly salaries in euros can be found on page 69 and following of the PDF download FIXED-TERM: The position is limited to 31.05.2028 SUPPORT
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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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, 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