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candidate will focus on implementing efficient and scalable data storage modes for large-scale mass spectrometry (MS) data and on development of improved and interactive visualization of MS data. In addition
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by: 25/11/2025 Are you looking for an opportunity to create significant impact and influence across a new large data set? We are offering a unique opportunity to drive the development, capture and
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large data set? We are offering a unique opportunity to drive the development, capture and iterative improvement of both a large data set model, and the techniques to generate informative report-based
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; Experience with large language models; Experience with the management and analysis of climate and/or health data; Be willing to work in an inter-disciplinary project team within a highly collaborative research
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locally, in the cloud and in the Galaxy workflow system. The successful candidate will focus on implementing improved data representation and storage modes for large-scale mass spectrometry (MS) data
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17 Sep 2025 Job Information Organisation/Company ERATOSTHENES CENTRE OF EXCELLENCE Department Big Earth Data Analytics Research Field Computer science Engineering » Computer engineering Computer
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11.07.2022, Wissenschaftliches Personal The professorship “Big Geospatial Data Management” is seeking to fill two research assistant (E13) positions to support research activities around geospatial
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posts) [Appointment period: each for six months] Duties We are building a domain-specific Multimodal Large Language Model (MLLM) devoted exclusively to hepatocellular carcinoma. The appointees will
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Fixed Term: Until 31 August 2029 Full Time: 35 hours per week The Opportunity: A Postdoctoral Researcher Position in Data Science/Molecular Epidemiology is available within the Marioni Group
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
modeling of large-scale dynamics in networks. This role involves creating large scale models of dynamic phenomena in electrical power networks and quantifying the risk of rare events to mitigate