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focuses on developing and applying digital technologies such as additive manufacturing/3D printing, numerical modelling, artificial intelligence, automation and robotics, digital twins, and smart materials
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models and reinforcement learning models for 3D graphs of materials to explore vast inorganic chemical spaces and design synthesizable energy materials. You will couple such models with physics simulation
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, brain analysis, 3D movement analysis, respiratory and circulatory examinations, sensory and motor functions analysis, etc. All study programs at Aalborg University involve problem-based learning
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degree in engineering, preferably supported by first academic publications Documented experience with simulation and Digital Twins Strong mathematics skills and practical knowledge in 3D modelling Strong
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validated in half-cells and full working batteries at industrial partners at TRL 6. Our objectives: Multiscale modelling to better understand RFB behavior and identify optimal hierarchical shaped pore- and
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on the development of AI models for analysis of cardiac CT scans, with the aim to explore how machine learning models can quantify cardiovascular disease and predict future events from CT scans. The project will
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Kontogianni. Our research explores how intelligent systems can perceive, understand, and interact with the 3D world. We develop new methods in computer vision, machine learning, and multimodal 3D
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PhD fellowship at the Copenhagen Center for Glycocalyx Research at the Department of Cellular and Mo
bioinformatics tools, validated gene editing protocols, glycoengineered cell libraries, 3D organ and tissue models, and design matrices for the recombinant production of protein-therapeutics. CGR also hosts