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Are you passionate about combining the directed evolution of diverse biomolecules with deep learning approaches and contributing to the development of better (bio)catalysts and drugs? We
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and product improvement, materials, processes and (mechanical) systems. ME is a dynamic and innovative faculty with high-tech lab facilities and international reach. It’s a large faculty but also
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opportunities for innovation and support well-founded decision-making within the programme. You design and organise engaging workshops and learning experiences for students, connecting digital and technological
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Engineering, Bioinformatics, Statistics, Applied Mathematics, Physics, or a closely related STEM field. Demonstrated experience developing AI and machine learning models for biomedical applications. Job
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side by side with experienced researchers who are committed to teaching, guiding, and supporting our newest hires, learning how we think about experiment design, dataset generation, time series analysis, feature
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and sustainability; Investigate and apply artificial intelligence and machine learning techniques, including large language models (LLMs), across CENSE’s scientific body in its five thematic areas
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for candidates with a background in or demonstrated ability to learn about: Bayesian methods, probabilistic machine learning or inverse modelling. Prospective applicants are encouraged to direct informal inquiries
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knowledge for a better world. You will find more information about working at NTNU and the application process here. About the position We have a vacancy for a PhD candidate in machine learning
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the project provide a strong foundation for careers in academia, research institutes, and the semiconductor and high-tech industries. Are you interested in knowing more about our section or department? Please
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of tax data. Core goals include creating a semantic repository for storing and indexing tax documents, designing machine learning algorithms to represent data in embedding spaces, and building tools