156 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "UCL" positions at Forschungszentrum Jülich
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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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laboratories Based on the complexity of the project, motivation to learn new skills to expand scientific knowledge High degree of analytical working style Excellent communication skills and ability to work in an
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the following areas desirable but not essential: electrocatalysis, rheology, coating technology, machine learning Intrinsic motivation to show initiative, creativity, and to work independently Excellent
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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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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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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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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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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