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for more than one PhD Research Fellowship period at the University of Oslo. Place of work is IFI (Informatics Department) / PT (Programming Technology), at Blindern, Oslo. Project description Automatic
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research questions. Strong quantitative research skills and proficiency in Python or R. Experience with large-scale textual data, natural language processing, machine learning, transformer-based models
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. The project is supervised by Associate Professor Ulysse Côté-Allard at the Department of Technology Systems, University of Oslo, whose research focuses on the development of machine learning algorithms
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Sensing (DAS) data processing and compression using ML Physics-driven machine learning for geophysical modeling and inversion Thus, the candidate is expected to have or about to have a PhD in a relevant
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of a PhD in Food Science, Food Chemistry, Food Engineering, or a closely related discipline An established research profile with national recognition and a strong record of publications in high-quality
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systems to rapidly traverse evolutionary landscapes Evolution of complex, multi-gene phenotypes Engineering plug-and-play selection systems for continuous evolution of diverse phenotypes Learn more at How
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broad implications for how we understand and engineer life. Learn more at How to apply Applications will be reviewed on a rolling basis. In your cover letter, please clearly explain your fit, interest
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machine learning. The successful candidate will develop and apply methods that integrate multimodal molecular and clinical data (genomic, epigenomic, transcriptomic) across serial patient timepoints
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Inverse Methods and Ionospheric Modelling Research Fellow - School of Engineering - 106995 - Grade 7
to completion) relevant to empirical modelling (any discipline), machine learning (any discipline), inverse methods (any discipline), ionospheric modelling and/or ionospheric measurement techniques, radio
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processes of the study systems of our collaborators. Core components of the research involve, among others, Bayesian hierarchical modelling, shrinkage methods, machine learning (ML) or dimension reduction