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appropriate conditions, it provides a confidence set (credibility set if prediction is Bayesian) for a multivariate estimate with statistical coverage guarantees. This PhD project aims to develop new CP methods
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physics models, Bayesian inversion methods, and machine learning algorithms in the electromagnetic context. Qualifications and personal qualities: Applicants must hold a master’s degree (or equivalent) in
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can support the structuring and modelling of knowledge in practical contexts. Participate in the development of IMF by bringing in methods from formal ontology and philosophical logic, yet tailored to
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publication of research or research communication. Use of computer programming as a method of research and inquiry. Applicants will be evaluated according to admission requirements in The Faculty of Humanities
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communication. Use of computer programming as a method of research and inquiry. Applicants will be evaluated according to admission requirements in The Faculty of Humanities' PhD Program pt. 2.1 . Applicants must
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developing data analysis methods, so the PhD candidate will benefit from collaborations within the group. Roles and responsibilities of the PhD fellow The candidate’s specific research tasks will include
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should have hands-on experience with a broad range of methods in molecular biology, cell biology, and biochemistry that may include mammalian tissue culture, immunoprecipitation and confocal microscopy
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Fellowship at the University of Oslo. Project description The net uptake of carbon to terrestrial systems (LULUCF) in Norway is estimated to be 20-25 MtCO2e/yr or about 50% of the anthropogenic greenhouse gas
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sequencing-based methods for immunological characterization. Fluent oral and written communication skills in English. Preferred selection criteria A strong background in imaging techniques and proteomics
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in-depth qualitative analyses but also mixed-methods approaches, possibly enabled by emerging AI-enhanced techniques. The PhD project should overall contribute to a better understanding collaborative