87 computer-programmer-"https:"-"UCL" "https:" "https:" "https:" "https:" "https:" "https:" "P" "I.E" PhD scholarships at Nature Careers
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, computer science, medicine, pharmacology, and physics. ISAS is a member of the Leibniz Association and is publicly funded by the Federal Republic of Germany and its federal states. At our location in
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2,900 work in administration and organisation. We are looking for a/an University assistant predoctoral/PhD Candidate Optical Quantum Computing and Machine Learning 51 Faculty of Physics Startdate
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learning programme to foster our staff’s soft and technical skills Multicultural and international work environment with more than 50 nationalities represented in our workforce Diverse and inclusive work
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or neurological diseases? Driven to strengthen your scientific creativity and independent thinking? Up to 18 PhD positions are open in the international Marie S. Curie Doctoral Programme ‘Interdisciplinary Marie S
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candidate will contribute to a fundamental research program exploring the chemical and physical principles of peptide ionization and fragmentation in LC-MS/MS – with particular emphasis on the analytical
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with a track record of intense mutual collaboration Structured PhD-training programme with a wide range of and professional development opportunities A respectful and appreciative work environment within
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You apply through our recruitment system on 30 March 2026, at the latest (https://umu.varbi.com/en/what:job/jobID:905098/ ). The application, written in English, should include: A short (max 2 pages
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Smet ( https://desmetlab.sites.vib.be ) and F. Van Breusegem ( https://vanbreusegemlab.sites.vib.be ) have expertise in abiotic stress signaling and in advanced molecular profiling, including mass
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our structured training program and participate in regular scientific exchange e. g. in retreats, colloquia and in tailored workshops. Requirements Candidates should hold an outstanding Master’s
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About us VIB.AI, the VIB Center for AI & Computational Biology, is a research center dedicated to integrating machine learning with deep biological insight to understand complex biological systems