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in a PhD thesis submitted to the University of Oslo. We seek a highly motivated candidate with a strong background in molecular biology, human genetics/genomics, data mining, machine learning, and
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single-cell and single-nucleus multi-omics, machine learning/AI, computational biology, and experimental validation to understand cardiovascular disease progression and identify novel therapeutic targets
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will focus on efficient probabilistic analysis of high-dimensional and dynamic systems, including advanced sampling, surrogate modelling, and AI or machine-learning methods where appropriate. Key
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theoretical statistics, statistical machine learning, high-dimensional inference, empirical process theory, stochastic modeling, optimization, or related areas. • Experience with writing and developing
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imaging science or related field or an equivalent qualification; (ii) demonstrate research skills in imaging science and machine learning, particularly on image reverse engineering, fake image and video
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research in machine learning. To produce research reports and/or publications as required by the funding body or for dissemination to the wider academic community. To provide guidance and support to any
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skills, including statistical analysis using R, Python, SPSS, Stata, or equivalent software. • Experience with computational social science methods, including NLP, machine learning, LLMs, social media
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://dimag.ibs.re.kr/ Successful candidates for the research fellowship positions will be new or recent PhDs with outstanding research potential in all fields of Discrete Mathematics with emphasis on Structural Graph
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. To collaborate with industrial and academic partners. To perform any other duties related to the research program. Job Requirements: Preferably PhD in Computer Engineering, Computer Science, Electronics
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, with opportunities to lead analyses, develop new expertise and contribute to high-impact scientific publications, and must have: A PhD, or equivalent research experience, in computer sciences