627 machine-learning "https:" "https:" "https:" "https:" "https:" "Dana Farber Cancer Institute" positions in Norway
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complex biological systems. Research Environment & Collaboration The successful candidate will work at the interface of machine learning and biostatistics, developing new theory, algorithms, and scalable
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, Mathematics (Operations research) or Computer Science or Machine Learning). The master thesis must be included in the application. Documented proficiency in English, please see requirements below. Requirements
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into reliable information about structural and aerodynamic behaviour remains a challenge. The PhD will develop data-driven methods that combine measurements, physics-based models, and machine learning to extract
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, Sport Management, and Economics. We have about 315 employees and 3 800 students at our campuses in Molde and Kristiansund. Read more about us: https://www.himolde.no/ The PhD position is organized under
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: simulation and risk modelling using advanced statistical and machine learning based methods. strategic portfolio management and dependency structure modelling for financial assets. effects of climate change
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the application. Details about the groups may be found using the following links: https://www.mn.uio.no/math/english/research/groups/several-complex-variables/index.html https://www.mn.uio.no/math/english/research
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, you will conduct cutting-edge research in these areas. You will learn state-of-the-art techniques in formal methods and knowledge representation and apply them to high-impact use cases related
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related to models and multiple sources of data describing ecological dynamics. The PhD project will address the following aims: 1) Develop efficient tools for learning about models from data, 2
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Research PhD Research Fellow in Education: Learning, Reading, and Assessment in the Context of Generative AI Apply for this job See advertisement Job description Applicants are invited to apply for a 3-year
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Environment Convergence Environment. Clim-SHOCK investigates volcanic climate shocks from the past and places them into a future scenario. What can we learn from the past to improve future climate projections