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(CT). In this role the successful candidate will write and seek to publish the developed state-of-the-art algorithms in high quality medical image computing journals & conferences. The successful
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possible co-appointment in the Department of Computer Science. The project combines theoretical and experimental approaches to develop renewable DNA computing devices that perform multiple computations in a
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Program research grant, this project brings together complementary expertise of the Zhulyn (Canada), Lagha (France), vanOudenaarden (Netherlands) and Kelleher (US) labs for a team-driven, multidisciplinary
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career. At Rehab Research Program (RRP), GF Strong Rehab Centre, we are committed to fostering an inclusive, respectful, and equitable environment. We do not tolerate racism, sexism, misogyny
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on performance and project needs. Qualifications • For Doctoral Candidates: Master’s degree in Mathematics. • For Postdoctoral Researchers: Ph.D. in Mathematics, ideally with a background in one or more of the
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the effects of specialized root metabolites and mixing wheat cultivars on the microbiomes and vice versa the effects of the microbiomes on plant performance and the suppression of root parasitic nematodes. You
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://pritykinlab.princeton.edu ) develops computational methods for design and analysis of high-throughput functional genomic assays and perturbations, with a focus on multi-modal single-cell, spatial and genome editing
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39 Faculty of Computer Science Startdate: 07.09.2025 | Working hours: 20 | Collective bargaining agreement: §48 VwGr. B1 lit. b (postdoc) Limited until: 07.07.2026 Reference no.: 4408 The position
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 7 days ago
working in high performance computing environments. Understanding of satellite radar interferometry. Create a Job Match for Similar Jobs About The University of North Carolina at Chapel Hill Carolina rates
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interested in computational materials design and discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials