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emulators for accelerated forward modeling Advanced data-intensive machine learning and AI techniques for survey analysis Applications to major international surveys, including LSST (Rubin Observatory
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highly interdisciplinary setting combining microbial mutagenesis assays, mammalian cancer models, next-generation sequencing, bioinformatics, and machine learning. Experimental data will be integrated with
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service, or similar circumstances, or other forms of appointment/assignment relevant to the subject area. Application A complete application should include: A cover letter clearly describing your
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Chemical Biological Centre (https://www.umu.se/en/kbc ) at Umeå University and is affiliated with the national Centre of Excellence – Umeå Centre for Microbial Research (UCMR) (https://www.umu.se/en/ucmr
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. Experience in analytical techniques such as Raman, MALDI-TOF-MS, HPLC is welcomed Willingness to learn new techniques (e.g., MicroCT Scan, FIB-SEM). Specific Requirements The degree must have been completed
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matter observatory. Main responsibilities The postdoctoral candidate is expected to focus on statistical data analysis including machine learning, Monte Carlo simulations, operations and calibration
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organizations, military service, or similar circumstances, or other forms of appointment/assignment relevant to the subject area. Application The application should include: 1. A cover letter with description of
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information about us, please visit: www.dbb.su.se . Project description The candidate will develop machine learning (ML) strategies, primarily revolving around interpretable ML and generative AI, to study
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Associate Professor Florence So and funded by an ERC Starting Grant. General information about the project is available at https://www.florenceso.org/projects/ and https://www.gu.se/en/news/political
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apply Website https://su.varbi.com/en/what:job/jobID:887501/type:job/where:39/apply:1 Requirements Research FieldPsychological sciencesEducation LevelPhD or equivalent Research FieldPsychological