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Field
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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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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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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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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
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to support computations on GPU hardware with various types of Finite Element methods. This work is embedded in a research project considering structure preserving Finite Element methods for multiphase flows
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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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a strong drive to develop expertise and reach research goals. You thrive working independently, taking initiative and approaching tasks in a structured and systematic way. At the same time, you are a
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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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, as well as previous success in obtaining research funding, are considered merits. You are a structured individual with strong initiative, who enjoys working independently but also appreciates
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ICSE, SPLC, and ICSA during the past recent years. About the research project As a postdoc, you will conduct original research in the area of AI-based program construction or performance engineering, co