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(ML). This position offers an exciting opportunity to develop and apply cutting-edge computational approaches to large-scale biomedical datasets, with a focus on advancing precision medicine in cancer
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to the applications of mathematics in cryptography, computing, business, and finance. PAP covers many areas of fundamental and applied physics, including quantum information, condensed matter physics, biophysics, and
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-learning-based intrusion detection mechanisms based on collection of network telemetry/observability data in the CPS testbed. The role involves close collaboration with networking researchers integrating
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validate wearable data algorithms, software tools and pipelines supporting large-scale, multi-cohort research. It will contribute to the ProPASS federated data analysis platform, including testing
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offers an unusual opportunity to work at the intersection of Isabelle/HOL, the seL4-verified microkernel, information-flow security, and AI-assisted theorem proving. The project aims to develop a formally
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scientists. The NOMIS-ISTA Fellowship Program provides postdocs with the freedom to work on big unanswered questions and high-risk research at the intersection of two or more scientific disciplines. ISTA
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conducting systematic literature reviews, comparative social policy analysis, working with large-scale longitudinal data sets, set theoretic methods, or computational social science would be a distinct
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technologies into commercial products that solve big problems. We support research that universities, companies, and venture capital firms don’t fund because they view it as too risky. We prefer to use the word
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maritime networks from the sixteenth to the nineteenth centuries. It will bring together large quantities of previously fragmented archival evidence from across Europe to reconstruct patterns of maritime
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the analysis of crosslinking and immunoprecipitation (CLIP), ribosome profiling and RNA proximity-ligation data (e.g. hiCLIP). We are looking for an innovative, and highly motivated scientist who is keen to