Statistical Learning and Predictive Modelling for Better Understanding of the Change of Ecosystems
This project is to develop methods and algorithms for multivariate data modelling and predictive analysis. The candidate model types and methods used can be within the range of the state-of-the-art in general and generalised regression, linear and nonlinear dynamic modelling, system identification, and statistical learning (e.g. machine learning), based on which to adapt and develop new effective methods for the analysis of a class of temporal and spatio-temporal ecosystems data e.g. cod diet data and relative food web dynamic data in sub-Arctic ecosystems of the Barents Sea (BS).
Prospective candidates with background in Engineering, Computer Science, Mathematics, Biomedical and Bioengineering, Physics and related disciplines, with good academic performance records in their undergraduate/postgraduate studies, are encouraged and invited to apply for and get involved in an appropriate project.
Applicants can apply for a Scholarship from the University of Sheffield but should note that competition for these Scholarships is highly competitive. it will be possible to make Scholarship applications from the Autumn with a strict deadline in late January/early February. Specific information will appear: http://www.sheffield.ac.uk/acse/research-degrees/scholarships
How good is research at University of Sheffield in General Engineering?
FTE Category A staff submitted: 21.80
Research output data provided by the Research Excellence Framework (REF)
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