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Field
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take a leading role in quantitative risk and uncertainty analysis of food safety risk assessments and in the development and implementation methods for the structured elicitation of expert judgement
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learning (DL) workflows for extracting, curating, and learning from structured and non-structured polymer data, Develop data-efficient representations for polymers for use in ML models, Develop ML models
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14 DCs in cutting-edge methods, theory, and transferable skills. More information may be found on the ENERPOL home page at https://cordis.europa.eu/project/id/101226229 . The part of the project
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on CIGS, may degrade or loose efficiency. This includes investigating how the materials’ composition, their physical structure, interfaces between layers and interactions with the environment affect their
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materials design with advanced structural, spectroscopic, and electrochemical characterization methods to unravel how ions, charges, and molecular interactions govern doping efficiency and stability. By
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the interfaces in solar cells to gain insight into molecular adsorption and electronic structure Model X-ray absorption and Auger spectra to provide analysis of experimental results Writing scientific papers
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diodes (LEDs). A critical challenge in this field pertains to understanding the nature and role of atomic-scale (local) structure and dynamics, which underlie the photophysical properties
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structure and carry out work. Preferred qualifications Prior experiences of teaching and/or supervision of students' project work, knowledge in machine learning and deep learning, language technology
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information about SLU Grogrund, please visit: https://www.slu.se/om-slu/organisation/centrumbildningar/slu-grogrund/. The postdoctoral researcher will screen legumes (pea and faba bean) and cereals (oat) traits
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structures, and machine learning. The role offers opportunities to contribute to high-impact publications and to practical decision support tools for the road engineering community. Qualifications Requirements