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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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with demonstrated ability to implement and optimize AI/ML models for biomedical datasets. Preferred Knowledge, Skills and Abilities Mathematical Modeling: Strong foundation in numerical modeling, graph
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combinatorics, probabilistic combinatorics, graph theory, Ramsey theory, or combinatorial number theory. The successful candidates will join a community of researchers in the Combinatorics Group within the School
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reports and scientific papers. Job Requirements: Ph.D. degree in Computer Science, Mathematics, Information Theory, Cybersecurity, Electrical Engineering, or a related field. Strong publication record in
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in research/industrial projects. Prepare reports and scientific papers. Job Requirements: Ph.D. degree in Computer Science, Mathematics, Information Theory, Cybersecurity, Electrical Engineering, or a
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emphasis on Extremal and Probabilistic Combinatorics, Graph Theory, Discrete Geometry, Ramsey Theory and Combinatorial Number Theory. The initial appointment is for 1-2 years, with a starting salary of no
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Postdoctoral Fellow with Professor Morgane Austern. Professor Austern’s group focuses on research in high-dimensional statistics, probability theory, machine learning theory, graph data, Stein method, ergodic
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population genetics/genomics. The focus of this postdoc will be on the application of Ancestral Recombination Graphs (ARGs) for spatial population genetic inference. Our work combines computational and
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combinatorics, structural graph theory, and related fields. Qualifications and personal qualities Applicants must hold a master's degree or equivalent education in Mathematics (Combinatorics and/or Discrete
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be enrolled in the PhD programme at the University of Oslo or UiT The Arctic University of Norway. Integreat brings together more than 100 researchers from mathematics, statistics, machine learning