43 assistant-professor-computer-science Postdoctoral positions at Nature Careers in Germany
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through a master’s thesis/internship/etc.) at the interface of climate, ecology, and informatics scientific curiosity, independent thinking, and team spirit Willingness to travel for work (e. g
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BC-Information Theorist (m-w-d)_E Fachbereich Biologie, Chemie, Pharmazie - Institut für Biologie AG Hiesinger Research assistant (Postdoc) (m/f/d) limited to 29.02.2028 (end of project
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To strengthen our team in the division “X-Ray Imaging” in Berlin-Steglitz, starting as soon as possible, we are looking for a Postdoctoral research assistant (m/f/d) in the field of physics
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, microbiology, or computational science. Further information about available projects can be obtained by email to Prof. Franziska Faber (franziska.faber@uni-wuerzburg.de ) and Prof. Jörg Vogel (joerg.vogel@uni
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cells to generate advanced model systems for studying beta-cell maturation and failure. You will apply state-of-the-art molecular biology and tissue culture methods, along with next-generation sequencing
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of motion analysis in collaboration processing of images in preparation for image-based modelling Your profile PhD in physics, materials science, computer science, applied mathematics or a related field
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initiatives. Collaborate with a dynamic, interdisciplinary team that combines science, sustainability, and technology to create a better future. Help shape the next generation of sustainable materials inspired
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collaboration in research networks Your profile PhD in Bioinformatics, Computational Biology, Systems Biology, or a related discipline Experience in multi-omics data analysis and handling big data Experience with
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microbiome-based strategies to aid in the detection, monitoring, treatment, and prevention of human diseases (e.g., Li et al., Nature Metabolism, 2024; Ni et al., Cell Metabolism, 2023; Leung et al., Science
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combining experimental and computational approaches to: Characterize the evolution of cis-regulatory networks across primate brain development using novel single-cell omics methods Apply deep learning models