53 data-"https:"-"https:"-"https:"-"https:"-"CNR-IRIB-PALERMO" Postdoctoral positions in Sweden
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on bioinformatics analysis of spatial gene expression data as well as other modalities (i.e. microbiome; metabolites, proteins) generated using the Spatial Transcriptomics (ST) method, Spatial metaTranscriptomics
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3 Apr 2026 Job Information Organisation/Company Chalmers University of Technology Research Field Computer science » Computer architecture Computer science » Other Researcher Profile Recognised
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We are looking for a postdoc to join our team at the Division of Computer and Network Systems. Become part of our innovative group and contribute to exciting research in Computer Architecture within
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and automated floor-plan recognition, to fill data gaps and harmonise information from disparate sources. Learn more and watch our project video here: https://sb.chalmers.se/digital-material-inventories
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Do you want to contribute to groundbreaking research in the intersection of battery systems and data-driven optimization? This is an exciting opportunity for a postdoctoral position at the Division
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Join and help us to derive global forest biomass data from the European Space Agency’s Biomass satellite mission. If you have interests in remote sensing, machine learning and forests, this is the
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(http://www.hogberglab.net/ ). You will contribute to the Dura-store project that is a part of the European Innovation Council’s Pathfinder Challenge for DNA-based digital data storage, aiming to use bio
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observational data. Together with international collaborators the Inverse Modelling group develops and applies inverse modelling / data assimilation systems that employ a range of observations to constrain
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research area MERGE (https://www.merge.lu.se ), focused on climate modelling. Aerosol research has been conducted at Lund since the 1970s and is now a designated profile area at LTH (https://www.lth.se
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at: https://www.umu.se/en/department-of-computing-science/ Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data driven models