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qualifications and merits for the position are: • Knowledge and experience on image processing or computer vision • Knowledge and experience on generative AI • Knowledge of data driven methods for modelling and
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and computational methods within quantum mechanics and statistical physics with the aim to design alloys for rare-earth-free high-performance permanent magnets. You will use computational techniques
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vision, machine learning, deep learning and neural networks, as well as courses in python, GPU programming, mathematical modeling and statistics, or equivalent. We are looking for candidates with: A solid
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applications. Project description This PhD project focuses on advancing the field of multi-modal data analysis and generation, integrating computer vision, natural language processing (NLP), and machine learning
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collaborations. The research group The position is in Ben Murrell’s group in MTC, based in the Biomedicum, in Karolinska’s Solna campus. The lab has worked across the experimental/computational interdisciplinary
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machine learning and computer vision techniques to enhance data analysis, pattern recognition, modeling, and prediction. The role requires a solid understanding of fluid dynamics and heat transfer, as
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of image analysis and machine learning with a minimum of 90 higher education credits. Relevant courses include, for example, image processing, computer vision, machine learning, deep learning and neural
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the Royal Institute of Technology, Stockholm. Dahlin’s team works at the intersection between experimental and computational medicine to map blood cell development at the single-cell level. This is performed
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will combine state-of-the-art computer vision, modeling and archived specimens to determine biotic and abiotic factors driving spatial variation in molt phenology. It will use museum genomics to recover
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identify systems-level mechanisms in cancer that can be used to uncover new biomarkers, drug targets, and paths to drug resistance. The long-term goal of our lab is to enable computer-aided design of