800 computer-"https:"-"https:"-"https:"-"https:"-"UCL"-"UCL" positions at Nature Careers
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the molecular to cellular scale. We are looking to appoint an individual who has strengths in both experimental and in advanced computational methods in Cryo-EM. The post-holder will work in collaboration with
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Job Id: 11716 Fixed term of 3 years | Full-time with 100% | Salary according to TV-L E13 | Institute of Medical Informatics We are UKM. We have a clear social mission and, with our focus
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of Medicine (MD) programme and postgraduate programmes. The successful candidate will be expected to engage students through innovative teaching methods, fostering critical thinking, professional values and
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command of English. Key Responsibilities Develop, deliver, and regularly update content relevant to their field across the curriculum of the Doctor of Medicine (MD) programme and postgraduate programmes
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of the Doctor of Medicine (MD) programme, from pre-clinical courses to clinical rotations, as well as in postgraduate programmes. The successful candidate will be expected to engage students through innovative
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. Familiarity with public health challenges related to tuberculosis Familiarity with high-performance computing (HPC). About Working at the Crick Our values Everyone who works at the Crick has a valuable role to
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• Opportunities to build a competitive academic portfolio Minimum Qualifications: • MD, DO, or equivalent medical degree (or currently enrolled in an accredited residency/fellowship program), or advanced
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PhD in computer science, physics, mathematics, or a related discipline Strong expertise in image analysis and/or machine learning, with a deep understanding of how data characteristics impact downstream
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Solution Architect Reference number: 2026-0039 The Division of Intelligent Medical Systems (IMSY) is an interdisciplinary research division working at the interface of computer science, physics, statistics
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within databases. Develop web services to query the database. Develop and implement computational and systems biology approaches for network and hidden driver analysis for genomic transcriptomics