323 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "Univ" research jobs in Sweden
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on developing and applying methods for 3D digital modelling, ranging from the collection of digital data from archaeological remains to the creation of 3D reconstructions of these environments. The second part is
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, uncertainty quantification, and scientific machine learning. The research will primarily focus on probabilistic methods for data-driven model reduction, with an emphasis on maintaining physical consistency
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). The project focuses on developing computational models for cancer risk assessment, integrating multiple types of data and risk factors. The main objective is to design and apply machine learning and deep
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Arctic headwaters. It will include field studies and the use of existing data, potentially complemented with modelling. The postdoc is also expected to collaborate with project partners and carry out
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interdisciplinary research environment More information about Jönköping University as a workplace, conditions and benefits on www.ju.se . Required Qualifications Applicants must have been awarded a PhD in Computer
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, data analysis, as well as interpretation and presentation of research results. The main part of the work for the advertised position involves studies of specific antibodies and antibody repertoires as
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Contact information to trade union representatives . To apply for the position Log into KTH’s recruitment system in order to apply for this position. You are responsible to ensure that your application is
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exposures and breast cancer risk. The focus of the project is on analyses of high dimensional and longitudinal data. The position is for three years, starting summer/fall of 2026 or upon agreement
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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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evaluated and validated using uniquely integrated historical datasets comprising environmental, social, demographic, mobility, and epidemiological information. The successful candidate will contribute to a