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-L, level 13 Supporting of mobility with a jobticket for using the public transport 30 days of vacation Participation in the benefits program for employees („Corporate Benefits“) The advertised
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-responsive proteins to develop genetic switches and circuits in therapeutically relevant bacteria. Your tasks: Use computational tools to design protein structures with desired properties Produce
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: completed scientific higher education degree (PhD) in the field(s) of Earth system science, physics, climate physics, geosciences, mathematics, computer science or a comparable field demonstrated experience
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on two core but complementary areas: Computer vision and sensor data analysis, applied to tasks such as object detection in drone images (e.g., pest or disease detection), object tracking (e.g. leaves
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– annual special payment – collectively agreed vacation entitlement – company pension plan Senckenberg is committed to diversity. We benefit from the different expertise, perspectives and personalities
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coding experience with e.g. Python/Matlab/R Practical experience with High Performance Computing, and scientific programming and a willingness to learn to work with high-performing computing systems
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populations. This project will explore the genetic and evolutionary mechanisms shaping adaptation through a combination of genomic, computational, laboratory, and field-based approaches. Research focus
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The INM – Leibniz Institute for New Materials in Saarbrücken, Germany, is an internationally leading center for materials research, a scientific partner to national and international research institutions, and a research and development provider for numerous companies throughout the world. The...
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Opportunities for personal and professional development Interesting, varied and challenging tasks and family-friendly working conditions Company pension plan (VBL) Company health promotion and the opportunity
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and skills: You hold a PhD in Bioinformatics, Computational Biology, Genomics or a related field. You bring proven expertise in deep learning and statistical modelling of biological data. You have