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for aircraft systems. You will integrate heterogeneous data streams (from multiple sources, such as sensory, physics model, etc.), flag impending failures, pinpoint and trace back the origin of system faults
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. Desirable: Proficiency in scientific programming (e.g. Python) and familiarity with data science and machine learning techniques. Experience with geochemical analytical techniques and working in a laboratory
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quantitative and digital methods, such as descriptive/inferential statistics, data modelling, machine learning (ML), experimental prototyping and technology ideation. A significant degree of autonomy is required
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learning Apply for this job See advertisement About the position Integreat – Norwegian Centre for Knowledge-driven Machine Learning is seeking a motivated PhD candidate in machine learning, knowledge
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institute that has developed an innovative collaboration research model, which seeks to create knowledge and influence thinking so that people can lead healthier lives. ISCRR conducts and facilitates research
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include (i) supervising law students in related matters and serving as a mentor and role model to law students in the clinics; (ii) helping to design and teach clinic seminar classes; and (iii) sharing in
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health data, such as electronic health records or biobank-scale resources (e.g., UK Biobank, All-of-Us, FinnGen). Familiarity with machine learning approaches, such as penalised regression, deep learning
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of the project is to provide a timely update and modifications of this model, as new science and methods have evolved since its development. Learning Objectives: The selected fellow in this project will have the
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research interests in one or more of the following subfields: scientific machine learning, optimization, deep learning, uncertainty quantification, (Bayesian) inverse problems, reduced order modeling, high
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software testing and Machine Learning components. This research will have real-world impact through close collaboration with industry partner and offers the opportunity to contribute to cutting-edge