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Field
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many areas of applied and theoretical statistics and data science, and is heavily involved in research at the crossing of statistics and machine learning. The focus of this postdoctoral fellowship is to
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Successful candidates will have publications in information theory and machine learning venues, such as IEEE Transactions on Information Theory, ISIT, NeurIPS, ICML, ICLR, and ACM FAccT. Experience in machine
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fellow will be part of a growing team of researchers, postdocs and PhD students working on intelligent observing systems using machine learning and data assimilation methods in the ACTIVATE project. UiO
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strategic career path, all PhD fellows are expected to submit a career development plan, specifying career goals and the competencies that the PhD fellow should acquire, no later than one month after
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analysis including econometrics, statistics and machine learning and related disciplines handling large amounts of complex data. They should provide evidence of potential for research and publication
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computer vision and vision-language models Experience with ML evaluation metrics and benchmarking Proficiency in Python and deep learning frameworks (e.g., PyTorch) Interest in applied, industry
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aberration-corrected electron microscopy, pair distribution function as well as XRD refinement to demystify the microstructure-property of new composition-disordered thermal materials and apply machine
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effectively exploited, possibly using some kind of machine learning algorithm, provides more accurate data than traditional data collection methods, e.g. paper-based surveys. This data is valuable to several
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of statistics and machine learning. Modern vessels produce vast amounts of multivariate data streams. The project addresses the development of trustworthy statistical and machine learning methods for anomaly
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interaction. As a Research Fellow you will hold (or be close to completion of) a PhD in signal processing, computer music, human-computer interaction (HCI), computer science, artificial intelligence, electronic