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collaborative and interdisciplinary activities. Job Requirements: Preferably PhD degree in Computer Engineering, Computer Science, Applied Mathematics or equivalent. Strong academic background in machine learning
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their PhD prior to commencing the role. The position can commence at any time during 2027, with the appointment being for a period of two years from the successful applicant's commencement date. About you
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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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• PhD in Mathematics, Applied Mathematics, Statistics, Operations Research, Quantitative Finance, or a closely related discipline. Skills: • Strong background in probability theory, stochastic
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perspective to the decision-making process in Congress. Fellows must be cognizant of and demonstrate sensitivity toward political and social issues and have a strong interest in applying personal knowledge
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the supervision and mentoring of PhD students to develop the next generation of academics (as per the norms of the discipline). Present results of research at conferences and other venues. Align with and actively
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criteria A PhD or equivalent in data science, social network analysis, mathematical modelling, statistics, or related discipline Demonstrated publication track record in data science, social network analysis
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to related research activities within the lab. Supervision of students in relevant topics. Job Requirements: PhD in Civil Engineering, Structural Engineering, Engineering Mechanics, Applied Mathematics, or a
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duration of 3 years. *Candidates who have not yet been officially awarded their PhD will be appointed as Research Assistant within the salary range £45,399 - £48,876 per annum. In addition to completing
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to verify the theoretical results. Job Requirements: Applicants should possess a PhD degree or have satisfied all the requirements for a PhD degree in Mathematics. Applicants should have deep knowledge in