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innovative development and application of novel data-driven methods relying on machine learning, artificial intelligence, or other computational techniques. More specifically, at NRM this research will be
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multiphase flow behavior. The project also involves applying machine learning and computer vision techniques to enhance data analysis, pattern recognition, modeling, and prediction. The role requires a solid
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related fields. Experience in Machine Learning/AI, mathematical, computational and statistical training are also advantageous. About the employment The employment is a temporary position of two years
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flow behavior. The project also involves applying machine learning and computer vision techniques to enhance data analysis, pattern recognition, modeling, and prediction. The role requires a solid
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, or computer science. Documented experience in professional software development. Documented experience or interest in Artificial Intelligence and Machine Learning development, Proficiency in written and oral
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registries and biobanks. The applicant is expected to have a strong computational focus on innovative development and application of novel data-driven methods relying on machine learning, artificial
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data reflect real‑world disease phenotypes. Advanced analytics: apply AI and machine‑learning techniques (e.g., graph neural networks, multimodal transformers) to uncover novel biomarkers and generate
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, reconstructed via X-ray computer tomography. Qualifications In accordance with the European Union’s funding rules for doctoral networks, applicants must NOT yet have a PhD. To qualify as a PhD student, you must
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facilitate data sharing among actors involved in a new circular flow of flat glass. Within the project, two PhD students, one at the Department of Computer and Information Science (with computer science
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research activities. For information about the Department of Physics, see: www.fysik.su.se/english . Project description Subject: Theoretical Physics We invite applications for a PhD position in the theory