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UiO/Anders Lien 20th October 2026 Languages English English English Join the University of Oslo for a PhD in Biomaterials! PhD Position – AI-Driven Multimodal Analysis and Predictive Modelling
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technological progress in our increasingly digital, data- and algorithm-driven world. Integreat develops theories, methods, models, and algorithms that integrate general and domain-specific knowledge with data
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AI-driven quantitative analysis and predictive modelling of biomaterials, tissues, and regenerative constructs. The project combines advanced 2D and 3D bioimaging, including micro/nanoCT, confocal
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technological progress in our increasingly digital, data- and algorithm-driven world. Integreat develops theories, methods, models, and algorithms that integrate general and domain-specific knowledge with data
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to quantify the evolution of ocean-continent subduction orogens and comparing first order model results with mountain belts on Earth. Investigating how the combination of tectonics and climate-driven surface
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combining field sampling with laboratory experiments and statistical modelling to quantify genetic variation in complex traits, the candidate will test whether these contrasting histories have driven
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of control systems theory to create a new generation of intelligent underwater robotic systems. The research will focus on developing hybrid learning–control architectures that integrate model-based control
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to strengthen resilience, preparedness, and fair allocation; (2) establishing site-specific, stakeholder-led strategies; (3) creating seasonal and long-term foresight by developing and coupling a suite of models
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for probabilistic unsupervised learning for structured biological data. The successful candidate will: Develop probabilistic factor models and scalable inference algorithms for structured biological (multi-view) high
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. This highly innovative project aims to develop a fully AI driven digital twin that enables real-time optimization and control of fermentation processes. The candidate will develop the digital twin for microbial