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outputsDisseminate results through scientific publications in top AI tier-venuesProvide guidance to PhD and MSc studentsContribute to teaching and advanced seminarsDesign and executing large-scale experimental
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experience with proteomic or lipidomic analysis is required. Experience with data-analysis with R is required; You are highly motivated, creative and willing to work with human blood samples and large -omics
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external) is reimagining the experimental neuroscience pipeline with big data and AI at its core. A central goal of the project is to build a foundation model of the visual brain—a “digital twin” that
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focused on bioinformatics and computational analyses of large amounts of data generated within biological, biomedical and biotechnological and life sciences area. We strive to gain new knowledge and drive
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and application of deep learning models from drones and satellite remote sensing data to identify urban risk areas for mosquito-borne diseases in East African cities. This position is part of an inter
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of applying them to data. Collaborative endeavours with members of the IPMU and Oxford groups is highly encouraged. You will have the opportunity to teach. Applicants should have a PhD (or close to completion
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, Computational Biology, or a related field. Biological Sciences research experience Extensive experience with bioinformatic analysis, big dataset analysis, data interpretation Extensive experience with single-cell
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developing next-generation AI methods for healthy climate adaptation. The position will focus on building and evaluating foundation models for large-scale spatiotemporal health and environmental data. Our team
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of atomic environments Detection of extrapolation and low-reference data regimes Active learning in configurational and chemical space Training and benchmarking of large-scale foundational MLFF models More
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27 Feb 2026 Job Information Organisation/Company FAPESP - São Paulo Research Foundation Research Field Educational sciences Researcher Profile Established Researcher (R3) Application Deadline 30 Mar