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- NTNU Norwegian University of Science and Technology
- NTNU - Norwegian University of Science and Technology
- Norwegian University of Life Sciences (NMBU)
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- Western Norway University of Applied Sciences
- NORWEGIAN UNIVERSITY OF SCIENCE & TECHNOLOGY - NTNU
- University of South-Eastern Norway
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Field
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of and interest in physiology, ethology and animal welfare Experience with data management and statistical analysis. Strong interest in data analysis and statistical methods will be emphasized. Interest in
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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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metrics directly into the design loop to drive durable, resource-efficient components, validated against analytical benchmarks or experimental data. Where to apply Website https://www.jobbnorge.no/en
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studies in collaboration with partner organisations (e.g., schools and public and private sector actors), alongside rigorous analysis of the collected data. Duties of the position The candidate will be a
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motivation for the position you have experience in artificial intelligence, data analytics, uncertainty analysis, probabilistic modelling, or statistical learning you have experience working on relevant
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to evaluate and inform digital health interventions for women at increased risk of GDM. The project will primarily utilize data collected from a completed randomized controlled trial (https://bump2babyandme.org
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to evaluate and inform digital health interventions for women at increased risk of GDM. The project will primarily utilize data collected from a completed randomized controlled trial (https://bump2babyandme.org
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knowledge for a better world. You will find more information about working at NTNU and the application process here. About the position A new PhD fellowship in path signatures, stochastic analysis, and
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intelligence, data analytics, uncertainty analysis, probabilistic modelling, or statistical learning you have experience working on relevant research or project activities involving machine learning or data
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of radionuclides and chemical contaminants in various samples (water, biota and sediments). Assessing combined effects (toxicokinetic and toxicodynamic) Organization and analysis of data, writing and dissemination