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position. Preference will be given to candidates working in areas of Mathematics that support our PhD program. Our PhD program has active researchers working in many areas. Pure Math: Combinatorics, Number
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perform research and teach one undergraduate Math or Stats course each semester. In addition, this position will conduct research under the supervision of Dr. Xiaolong Li. The research effort will focus
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the application. Details about the groups may be found using the following links: https://www.mn.uio.no/math/english/research/groups/several-complex-variables/index.html https://www.mn.uio.no/math/english/research
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Carleton University | Government of Canada Ottawa and Gatineau offices, Ontario | Canada | 19 days ago
tools or for resources day-to-day work requirements, as needed. About Carleton University Carleton University is a dynamic and innovative research and teaching institution with a national and
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and education activities in two divisions: Division of Mathematical Sciences (MAS) and Division of Physics and Applied Physics (PAP). MAS covers diverse topics ranging from pure mathematics
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% super Pioneering multicellular interface modelling within an open source framework and three translational case studies. Apply now to develop unified multicellular modelling tools and drive experimental
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for an additional year; they incur a teaching load of two recitations per semester. Application Instructions Applicants will submit a cover letter, curriculum vitae, a statement on current/future research interests
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to prepare fellows for doctoral research. Fellows will be exposed to a range of research tasks across various stages of the research process. Faculty mentors are committed to the mentorship and
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outstanding opportunities for continued scientific development and for contributing to pioneering research. Specifically, the fellow will apply as well as develop code to process and analyze mycobacterial
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related to models and multiple sources of data describing ecological dynamics. The PhD project will address the following aims: 1) Develop efficient tools for learning about models from data, 2