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factorization methods, the candidate will be positioned at the forefront of genetic data science. For more information and how to apply: https://www.jobbnorge.no/en/available-jobs/job/297729/phd-position-in
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network that equips early-career researchers with solid theoretical foundations, advanced empirical methods, and the ability to engage across disciplines. We welcome candidates with strong academic
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spatial structures, physical laws, high-dimensional imaging, and clinical covariates. Apply these methods to spatial transcriptomics and fluorescence imaging data to gain a more precise understanding of
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covariates. Apply these methods to spatial transcriptomics and fluorescence imaging data to gain a more precise understanding of complex biological systems. Research Environment & Collaboration The successful
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carcinoma as a use case. The successful applicant will develop Bayesian statistical and machine learning methods for treatment response prediction using clinical information, molecular characterizations
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carcinoma as a use case. The successful applicant will develop Bayesian statistical and machine learning methods for treatment response prediction using clinical information, molecular characterizations
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on the analysis of complex event history data, with some relevant topics being the analysis of outcomes under competing risks, studies of individual heterogeneity, estimation using machine learning methods and
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, methods, models, and algorithms that integrate general and domain-specific knowledge with data, laying the foundations of next generation machine learning. We do this by combining the mathematical and
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) Read more about the areas here: https://healenae.eu/phd-calls The candidate must develop a project description, which should be submitted as a part of the application. The description should show how
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, with about half being women. It also includes around two dozen PhD students, Post-Doctoral and Research Fellows, and Associate Professors II. Read more about HUP section at: https://www.sv.uio.no/psi