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Studentships in Bayesian Learning
martes, 19 mayo 2020

I am seeking applications for two EPSRC DTP Ph.D Studentships that are available in the Department of Mathematics, at Brunel University London, to work with me, in the area of Bayesian learning. There is a possibility of offering the studentships to international and EU-based applicants, in addition to consideration of UK-based students. Please contact me at This e-mail address is being protected from spam bots, you need JavaScript enabled to view it , if interested. The brief project outlines are as below:

Sufficiently Deep Supervised Learning in High-Dimensions & Fast Prediction, with Applications
This project is dedicated to the application of a recently-advanced, dual-layered and free-form Bayesian supervised learning strategy that will be employed to learn the high-dimensional functional relationship between system parameters and a high-dimensional observed variable that affects said parameters. To reduce prediction times, while acknowledging complexities of real data, we will develop a classifier of the system parameter vector, given associated observable values, subsequent to the rigorous learning of the inter-variable relationship.
Bayesian Prediction, Notwithstanding Unattainable Learning Given Unavailable
Training Data
------------- This project addresses the need to predict values of a system-variable, at which a related observable is realised, even when learning the functional relationship between the system-variable and observable is impossible, owing to unavailability of training data, and ignorance about the probabilistic nature of relevant variables. We will employ a new Bayesian method to achieve such prediction in a dynamical system, by learning the function that drives evolution of the observable, and its probability distribution.

The Ph.D projects are set to start on the 1st of October 2020. Successful applicants will receive an annual stipend (bursary) of £17,285 plus payment of their full-time home, EU or international tuition fees for a period of 36 or 48 months (3 or 4 years).

Applicants will have or be expected to receive a first or upper-second class honours degree in an Engineering, Computer Science, Design, Mathematics, Physics or a similar discipline. A Postgraduate Masters degree is not required but may be an advantage. Experience in MCMC techniques, will be an advantage for these projects. The applicant should be highly motivated, and have good communication skills.

To apply, please submit your application documents (see list below) by Noon on Friday 12 June 2020 to This e-mail address is being protected from spam bots, you need JavaScript enabled to view it Interviews will take place in July 2020.
* Your up-to-date CV;
* Your personal statement (300 to 500 words) summarising your background, skills and experience;
* Your Undergraduate/Postgraduate Masters degree certificate(s) and transcript(s);

Thank you very much.