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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/english/about/organization/sections/hup
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The successful applicant should/must have experience in nutrition epidemiology, preferably in aging, and in statistical analyses applicants who have experience also in independent Publishing of research results
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Professors II. Read more about HUP section at: https://www.sv.uio.no/psi/english/about/organization/sections/hup/index.html Your main tasks will be: Work as part of an interdisciplinary research team with
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programming (e.g., Python) and working with Linux/Unix environments. Solid mathematical/statistical skills. Preferred/desired qualifications Experience in applying for ESA projects, Horizon Europe, and similar
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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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broad range of areas, including causal inference and time-to-event analysis, clinical trials, epidemiology, high dimensional statistics, infectious disease, machine learning and mathematical modelling
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techniques to identify the enzymes, as well as protein purification and recombinant protein expression to get hold of the enzymes. The PhD student will be part of the large and international PEP group (https
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experience in electrochemistry, or environmental engineering and water technology. Experience with multivariate data analysis, statistics, machine learning, numerical simulations, and programming. Skills in
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experiences and skills will be emphasized: Experience in plant phenotyping, drought stress research, or root biology Familiarity with imaging technologies, statistical analysis, or quantitative data analysis