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Field
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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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, Pennsylvania 19004, United States of America [map ] Subject Areas: Industrial Engineering Physics Statistics Mathematics Economics (more...) Management Science & Engineering Computer Science and Electrical
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on the topic assigned for each position. Requirements: outstanding university degree (typically M. Sc.) in Computer Science, Data Science, Statistics, Mathematics or another relevant field study with good GPA
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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
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, dynamical systems and statistical physics. The candidate will be jointly supervised by the Coventry team Dr Fei He and the Stellenbosch team Prof. Francesco Petruccione . This project will contribute
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the carbon footprint of milk production. The project will apply advanced statistical methods, artificial intelligence, and cutting-edge genetic models to support and enhance management and breeding decision
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. Experience in working with large data sets, knowledge of statistics, and some programming expertise is essential. The project is based in ECEHH, at the University of Exeter’s Penryn Campus in Cornwall, and may
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subject relevant to the proposed PhD project (such as mathematics or statistics) is our standard entry, however we place value on prior experience, enthusiasm for research, and the ability to think and work
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dimensionality reduction as well as analysis of blood proteomic data. We are looking for a student with experience in bioinformatics and relevant statistical methods using R or Python languages. Some knowledge of
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degree (or equivalent) in Data Science, Computational Biology, Bioinformatics, Computer Science, Physics or a related field Solid programming skills and knowledge in deep learning, statistical modelling