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for learning about models from data, 2) incorporation of expert knowledge in model building through Bayesian prior elicitation, and 3) develop new methods for identification of conflicts in different parts
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data-driven (AI/ML) approaches to analyse polymer physicochemical properties and degradation behaviour across different environments, with the aim of identifying key molecular-level design principles
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conducting the following activities: - Analyze the sensitivity of the seismic hazard model to parameters such as ground motion prediction equations (GMPEs) and site effects. - Investigate differences between
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for evaluation by the closing date. Only applicants with an approved doctoral thesis and public defense are eligible for appointment. Programming skills in GAMS and Python. Experience with HPC. Experience with
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knowledge of handling sequence data from RNA-seq or other NGS-based approaches. Experience with data analysis in Python/R. Experience with experimental work on plants or other eukaryotic organisms in
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., Seurat, Scanpy, DESeq2). Experience with spatial transcriptomics and multi-modal data integration is highly desirable. Proficient in Python, R, and ML libraries such as PyTorch or TensorFlow. Strong
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the Norwegian educational system A solid background in machine learning, mathematics, linear algebra, and/or statistics is also required Solid knowledge and experience in Python programming Experience with
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, Python). Knowledge of a Scandinavian language may be an advantage for teaching and supervision (if applicable). All candidates and projects will have to undergo a check versus national export, sanctions
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, Python, Julia, etc. Demonstrated experience working with spatial epidemiological, ecological, or environmental data, including hands-on use of GIS, spatial statistics, or other spatially relevant methods
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graphical and statistical tools (e.g. python, R). Willing to exchange with national and international project collaborators including potential research stays abroad. Very good co-operative skills, and the