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Field
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. Ideal candidates will have demonstrably strong research skills, evidenced by multiple publications in top-tier machine learning or artificial intelligence conferences and/or leading scientific journals
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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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will work closely with the Principal Investigator (PI), Co-PI, and the research team to develop deep learning-based computer vision algorithms and software for object detection, classification, and
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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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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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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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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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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
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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