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on human behavior modeling related to video classification using deep learning networks for end-users. Work with other team members to develop and maintain software for maximum efficiency and usability
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-funded AI research group “Fusing Deep Learning and Statistics towards Understanding Structured Biomedical Data (DeSBi)” development of deep neural networks and machine learning algorithms for the analysis
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translational medicine concepts Publication history in relevant fields (AI/ML, genetics, toxicology) Experience with deep learning frameworks and generative AI models (e.g., GANs, VAEs) Key Leadership
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/ EEA countries and exemptions from the requirements: https://www.mn.uio.no/english/research/phd/regulations/regulations.html#toc8 Grade requirements: The norm is as follows: The average grade point
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progression modelling, exploiting advances in deep feature learning and uncertainty quantification to support the Bayesian framework, as well as implementation of computational models of neurodegeneration
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understanding, anticipating, and managing risks posed by harmful algae to seafood production, public health, and consumer confidence. The successful applicant will have a PhD and demonstrated expertise in seafood
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spans quantum mechanics, statistical physics, and deep learning and aims to enable AI-guided predictions of synthesizable and functional materials such as energy storages, catalysts, smart-alloys, energy
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Information regarding postdoctoral fellow salary, which is determined by the number of years post PhD, can be found at https://postdoc.hms.harvard.edu/guidelines Minimum Number of References Required Maximum
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: Education: Bachelor in Biosciences, or Engineering degree in Computer or Data Sciences. PhD in bioinformatics, data sciences, machine learning or related areas. Experience: previous experience working with
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, and the ability to read related scientific papers on cancer combination therapy. It would also require expertise in relevant AI methodology, such as deep learning architectures for property prediction