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We have 46 stochastic PhD Projects, Programmes & Scholarships

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stochastic PhD Projects, Programmes & Scholarships

We have 46 stochastic PhD Projects, Programmes & Scholarships

Statistics and AI for Engineering and Smart Manufacturing

Overview. We are thrilled to announce an exceptional PhD opportunity in the dynamic fields of Statistics and Artificial Intelligence, specifically tailored for Engineering and Smart Manufacturing. Read more

Projects in mathematical systems and control theory

Mathematical systems and control theory “represents an attempt to codify, in mathematical terms, the principles and techniques used in the analysis and design of control systems” [1]. Read more

Modelling Convergent, Divergent and Oscillatory Phenomena in Social Dynamics

If you are interested in building mathematical models to explain social phenomena in a variety of contexts, from flocking animals to interacting crowds, and you have a strong academic background in applied mathematics or a related discipline, then this project could be perfect for you. Read more

Energy Storage Modelling

Supervisory Team.   Prof Andrew Cruden, Prof Richard Wills, Prof Suleiman Sharkh. Project description. Applications are invited for a fully-funded PhD studentship to investigate the electrical, thermal and economic modelling of a range of electrical energy storage types (e.g. Read more

Mathematical analysis of PDEs arising from modelling biological processes

Biological systems, in contrast to physical systems, exhibit a high level of complexity. Attempting to replicate their dynamics through mathematical models (system of differential equations) turns out to be quite challenging. Read more

Doctoral research positions available in mathematical sciences and its applications

 . SAMBa (the EPSRC Centre for Doctoral Training in Statistical Applied Mathematics), at the University of Bath, offers a 4-year PhD programme, including initial training to develop breadth of knowledge and research skills. Read more

Artificial Intelligence - Computer Vision

Our research efforts aim to understand the nature of visual perception so that we can create more visually intelligent machines. This is enabled via fundamental concepts in reasoning, prediction, supervised, semi-supervised and unsupervised learning, and stochastic optimization techniques. Read more

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