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UiO/Anders Lien 11th May 2026 Languages English English English PhD in Generative AI and Statistics: Coalescent-inspired diffusion models for discrete data Apply for this job See advertisement Job
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research and participation in international research conferences. Detailed information: https://www.kf.vu.lt/en/studies/studies-in-english/doctoral-studies/admission-for-phd Website for additional job
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This PhD project will focus on developing, evaluating, and demonstrating advanced data analytics solutions to a big data problem from aerospace or manufacturing system to uncover hidden patens
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thin films for emerging technologies. By combining state of the art physical vapor deposition, high throughput experimentation and data science, we develop thin films with enhanced performance and
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-ray Computed Tomography (XCT) has evolved into a significant "big data" challenge, with a single scanner easily generating over 10TB of data annually. The sheer volume of this structured data creates
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A large volume of data are routinely captured in high-performance training and competition environments. Whilst these data have the potential to inform key performance decisions, the full potential
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: cell and molecular biology, evolution and biodiversity, precision medicine and diagnostics, epidemiology and biology of infection. For more information, please see https://www.scilifelab.se/data-driven
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performance degradations and unwarranted system failures can occur. There is certain physical information known a priori in such aerospace platform operations. The main research hypothesis to be tested in
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to study and predict. In this four-year SNF-funded project, you will develop data-driven, multiscale simulation methods that combine computer simulations, machine learning, and surrogate models to explore