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Mathematics at the Faculty of Science, Forestry, and Technology invites applications for a Doctoral Researcher (PhD student) position in operator theory, complex analysis, and their applications to mathematical
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The primary objective of this project is to establish the evidence base on professional cycling road ‘racing’ trends and the critical tactical moments that determine how races are won. This evidence will inform future race strategy and live race tactics. Multiple factors influence the strategy...
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: Applications are invited from outstanding graduates who meet the following criteria: BSc, including mathematics, cell and molecular biology Honors (first class) or MSc degree in Microbiology/Immunology
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analysis. Experience in formulating and solving mathematical optimization problems is an asset. Proficiency in English is required; good comprehension and oral communication skills in German are desirable
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The successful applicant must have a Ph.D. in Computer Science, Data Science, Statistics, Mathematics, Physics, Engineering or related field at the start of the appointment. Type Benefited Staff Special
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genetics, and may include expertise in one or more of the following: computational biology, statistical genetics, population genetics, biomedical informatics, complex genetics, clinical genetics
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change theory and practice, implementation science, and associated measurement and analytic techniques. The candidate is expected to help bridge these domains using validated statistical tools. Applicants
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mathematical foundation of machine learning models. You will be responsible for developing scientific machine learning methodologies enabling new approaches for solving machine learning problems including
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should have or expect to achieve, at least a 2:1 (or equivalent) in any engineering degree programme, physics or mathematics. English language requirements: Applicants must meet the minimum
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multivariable statistical methods. Support for skills development is provided within the Horse Microbiome Research Group and the university’s Doctoral College . Delivery of this project in collaboration with