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embedding longevity, material efficiency, and realistic performance limits from the start. This project develops a pioneering methodology for data-driven optimization of next-generation material systems. You
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the University of Bergen, Norway. Supervisors: Prof. Martin Reincke , Prof. Nicole Reisch Location: Ludwig Maximilians University Hospital Munich, Germany Duration: 3 years (with possibility of extension) Start
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complex biological systems. Research Environment & Collaboration The successful candidate will work at the interface of machine learning and biostatistics, developing new theory, algorithms, and scalable
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candidate will work at the interface of machine learning and biostatistics, developing new theory, algorithms, and scalable implementations. By establishing a new class of multi-frame factorization methods
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, finger, and multimodal) under controlled and semi-wild conditions. Develop AI-based algorithms for biometric trust assessment, anti-fraud analytics, and secure onboarding. Lead the deployment and
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Intelligence (AI) were launched in the Fall of 2025. These centres will foster interdisciplinary collaboration among researchers to address both scientific and societal challenges, develop new technologies
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of 2025. These centres will foster interdisciplinary collaboration among researchers to address both scientific and societal challenges, develop new technologies, understand the impact of AI, and drive
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learning from sequential data is available at the Department of Mathematical Sciences at the Norwegian University of Science and Technology (NTNU) in Trondheim. The project will be supervised by Prof
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sequential data is available at the Department of Mathematical Sciences at the Norwegian University of Science and Technology (NTNU) in Trondheim. The project will be supervised by Prof. Kurusch Ebrahimi‑Fard