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Field
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, and semi-structured interviews. Using European competence models (e.g., GreenComp, LifeComp), the project will develop flexible lifelong learning models that universities can adapt to different
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, mechanistic simulations, and predictive AI models. Your work will help unlock new insights into disease mechanisms and inform potential treatments, diagnostics, and drug repurposing opportunities. Your role You
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for experimental projects conducted in the IWT. Disseminate research through publication in high-quality journals and leading conferences. If possible, develop or adopt numerical simulation models and methods
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development of next-generation computational tools for simulating particle-laden two-phase flows by integrating advanced Artificial Intelligence (AI) techniques with traditional computational fluid dynamics
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and modeling of properties and spectra of PFAS chemicals with the goals of improving detection of PFAS compounds, replacing PFAS in plasma etching processes, or identifying solid adsorbent additives
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learning to model solid-state materials while collaborating with experimentalists. Qualifications • Ph.D. in Computational Physics/Materials Science, or related field (completed by start date
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Responsibilities: Lead the design, implementation, and validation of data-driven Digital Twin (DT) models to simulate and forecast dynamic energy demands and renewable generation patterns within shipyard operations
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and testing solutions in a simulated clinical environment, ensuring the applicability of the developed models in clinical practice.; 4) Participate in the analysis of results and benchmark the system
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://www.uib.no/en/sefas About the project/work tasks: The postdoctoral research fellow will perform quantitative data analysis using advanced techniques such as signal processing and dynamic systems modeling, and
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application consists of: An application Transcript(s) – For this opportunity, an unofficial transcript or copy of the student academic records printed by the applicant or by academic advisors from internal