210 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" Fellowship positions at University of Oslo
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for a motivated PhD candidate to join our team at the University of Oslo for the project: DC14 - ESKAPE pathogen adhesion to cells and implant surfaces Supervisors Dirk Linke: https://www.mn.uio.no/ibv
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English requirements for applicants from outside of EU/ EEA countries and exemptions from the requirements: https://www.mn.uio.no/english/research/phd/regulations/regulations.html#toc8 Grade requirements
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particle models, stochastic PDE and models from fluid dynamics and machine learning. What skills are important in this role? Qualification requirements: The Faculty of Mathematics and Natural Sciences has a
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of EU/ EEA countries and exemptions from the requirements: https://www.mn.uio.no/english/research/phd/regulations/regulations.html#toc8 Grade requirements: The norm is as follows: The average grade point
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Language requirement: Fluent oral and written communication skills in English English requirements for applicants from outside of EU/ EEA countries and exemptions from the requirements: https://www.mn.uio.no
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: https://www.mn.uio.no/ibv/english/people/aca/dirkl/index.html Co-supervisors: Håvard Haugen (Corticalis AS), Susanna Fagerholm (University of Helsinki) Project description The PhD candidate will study
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(UiO). Learn more about working at PROMENTA here: https://www.sv.uio.no/promenta/english/ . Colourbox via Unsplash Colourbox Qualifications Required qualifications A PhD degree in psychology, human
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of partial differential equations (PDE). Examples of models in the scope of the project include particle models, stochastic PDE and models from fluid dynamics and machine learning. What skills are important in
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plus Language requirement: Good oral and written communication skills in English English requirements for applicants from outside of EU/ EEA countries and exemptions from the requirements: https
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pressure-build ups in potential multi-site storage licenses. The research will help to suggest best practices for machine learning integration in de-risking CO2 storage sites. We seek a candidate with a