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properly. Please turn on JavaScript in your browser and try again. 13th June 2025 Languages English English English PhD candidate in Statistics at the Oslo Centre for Biostatistics and Epidemiology (OCBE
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, Robotics, Computer Science, Statistics, or related discipline. Strong background in Machine Learning and Control Theory. Demonstrated experience in research projects with industrial partners. Excellent
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, pharmacoepidemiology, public health, statistics or a related field. The position offers excellent opportunities for scientific development within an international research environment. About the project/work tasks The
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motivated candidate with a background in neuropsychology, medicine, neuroscience, molecular biology, biostatistics, pharmacology, pharmacoepidemiology, public health, statistics or a related field. The
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epidemiology, causal inference, genetic epidemiology, and machine learning. As a PhD candidate in the project, you will: Actively participate in group meetings, design statistical analysis plans in collaboration
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degree (or equivalent) in biology, bioinformatics, genetics, molecular ecology, or a related field. A good knowledge and practical experience in molecular ecology, population genetics, and statistical and
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-on experience in benthic ecology and statistical analysis are required. Additional experience in analysing biodiversity data, ship-based sampling, marine plankton (preferably meroplankton), handling and analysing
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(or equivalent) in biology, bioinformatics, genetics, molecular ecology, or a related field. Solid knowledge and practical experience in molecular ecology, population genetics, and statistical and bioinformatic
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species and vegetation ecology), advanced statistical modelling using R software, and conducting fieldwork under harsh environmental conditions. A successful applicant should have good skills in English and
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knowledge about natural resource management Knowledge of software R Strong skills and/or interest in mathematical and statistical modelling is a strength Ability to conduct field work in remote alpine areas