Forecasting by Learning the Evolution-driving Function
Applications are invited for our EPSRC funded Doctoral Training Partnership (DTP) PhD studentship for the project “Forecasting by Learning the Evolution-driving Function” starting 1 April 2022. Successful applicants will receive an annual stipend (bursary) of £17,609, including inner London weighting, plus payment of their full-time tuition fees for a period of 36 -months (3 years).
Applicants must be classified as a home (UK) tuition fee paying student to be eligible for this studentship.
Forecasting of future states of a generic, non-autonomous dynamical system will be undertaken by predicting the evolution-driving function, at a test time point in the future. Such prediction will follow Bayesian supervised learning of this evolution-driver, that will be enabled after a novel generation of this function within design time windows that host temporally-local stationarity of the system. Applications to real-world data will be undertaken.
Please contact Dr Dalia Chakrabarty at email@example.com to arrange an informal discussion about the project.
Skills and Experience
Applicants with a background in Computational and Mathematical Statistics, and/or Probability. A strong computational background is relevant. Previous knowledge in Bayesian Statistics, Machine Learning, or Dynamical Systems, is not essential.
Academic Entry Criteria
You will have or be expected to receive a 1st class or 2:1 honours degree in Mathematics, Statistics, Physics or a similar discipline. A postgraduate masters degree is not required but may be an advantage.
How to apply
Please submit the documents below as a single PDF file by email to firstname.lastname@example.org by 14:00 on Monday 28 February 2022.
- Your up-to-date CV;
- Your personal statement (300 to 500 words) summarising your background, skills and experience. Please state the name of the project supervisor at the top of your personal statement;
- Your Undergraduate/Postgraduate Masters degree certificate(s) and transcript(s);
- Your English Language qualification of IELTS 6.5 overall (minimum 6.0 in all sections) or equivalent, if applicable;
- Contact details for TWO referees, one of whom can be a member of Brunel University academic staff.
Interviews will take place in early/mid-March 2022.
- My D.Phil (from St. Cross College, Oxford) was in Theoretical Astrophysics, and I was examined by Prof. James Binney. My doctoral thesis was dedicated to the development of a novel Bayesian learning method to learn the gravitational mass of the black hole in the centre of the Milky Way, (along with the Galactic phase space density), and to the computational modelling of non-linear dynamical phenomena in galaxies. Thereafter, I continued to develop probabilistic learning methods, and undertake Bayesian inference, within astronomical contexts, till 2009, when I moved to Warwick Statistics, and started developing Bayesian methodologies, to apply to diverse areas. After Warwick, I was a Lecturer in Statistics, in Leicester Maths, and then in Loughborough Maths. I moved to Brunel, Department of Mathematics, at the beginning of 2020. My current interest is strongly focused on the development of Bayesian learning methodologies, given different challenging data situations, such as data that is shaped as a hyper-cuboid; with components that are diversely correlated; absent training data; data that is discontinuously distributed and/or changing with time. I am equally keen on learning graphical models and networks of multivariate datasets, as random geometric graphs, with the ulterior aim of computing distance between a pair of learnt graphs. I am also interested in the development of Bayesian tests of hypotheses that are useful when the alternative model is difficult/impossible to perform computation within, and recently, have initiated a method of optimising the mis-specified parameters of a parametric model, while learning the desired model parameters. My current applications include areas such as healthcare, vino-chemistry, astronomy, test theory, material science, etc.