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- NTNU - Norwegian University of Science and Technology
- NTNU Norwegian University of Science and Technology
- University of Oslo
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that the PhD candidates complete their degrees within the nominal length of study an attractive and good learning environment for PhD candidates The programme offers several courses and candidates from other
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health, epidemiology, statistics, biostatistics, or machine learning/artificial intelligence. You must have a strong academic background from your previous studies and have an average grade from your
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for machine learning models to optimise membrane properties, structure, and fabrication. The fellow will play a key role in the experimental part of the project, including: Preparation and characterisation
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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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, epidemiology, high dimensional statistics, infectious disease, machine learning and mathematical modelling. The centre has numerous collaborations with leading biomedical research groups internationally and in
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while offering flexibility to tailor your training to specific needs and interests through elective courses and secondments. • Blended Learning Approach: Our training combines intensive in-person
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offering flexibility to tailor your training to specific needs and interests through elective courses and secondments. Blended Learning Approach: Our training combines intensive in-person workshops
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ability to systematically carry out goal-oriented work. Enjoy and contribute to interdisciplinary research. Keen interest in learning and working in teams. Good skills to deliver oral and written
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with algorithms for wearable data University of Manchester (UK): To learn mathematical modelling of hormone rhythms. University of Bristol (UK): To learn mathematical modelling of hormone rhythm
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. Strong (inter-)national network in field of application. Experience with high-performance computing (HPC) and large datasets. Experience with machine learning applied to geophysical signals. Experience in