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Options and goals for postgraduate study

Hi all. I'm looking for some advice about postgraduate applications.

I've just finished my second year at the University of York studying BSc Environmental Geography, and scored 76% in first year and 74% in second year. I know that I want to stay in research/academia, and am also currently undertaking a research internship over the summer. I've currently got my eyes set on Durham University's MSc by Research in Computational Geoscience, but have very specific goals for doctorate study. I am wanting to aim for the DPhil in Intelligent Earth at University of Oxford, but am unsure of my chances, since I was rejected by them when I applied for BA Geography there (and went on to achieve 3 A* and 1 A at A-level). What do you think my chances of success would be? And can anyone give some advice on how to make a competitive application? It's been my dream university ever since I started looking.

Thanks all :smile:

Reply 1

SS378

In addition to the grades, you will need three excellent letters of recommendation. Try to get some research experience and make sure you have a strong background in higher level mathematics and statistics. Learning how to program (python, C++) will be useful as well. Your undergraduate rejection has no bearing on your application to the DPhil, so don't let that discourage you.

Reply 2

ia123456

Original post by SS378

Thank you very much. For the background in higher level Mathematics and Statistics, is there anything specific you would recommend? I have completed some modules in Stats at university, but nothing that was harder than the A-level Maths (A*) and Further Maths (A) I did.

Reply 3

SS378

You're welcome. For the mathematics a solid grounding in Calculus, linear algebra, probability and statistics will certainly not hurt. Cambridge's machine learning degree recommends:

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Calculus and University-level Mathematics: differentiation, integration, vector calculus, ODEs/PDEs, Fourier series, vector gradients, coordinate systems, etc.

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Linear algebra: vectors, matrices, linear transformations, matrix inversion, eigenvalues and eigenvectors, matrix factorization, SVD, least squares solutions, etc.

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Probability and Statistics: random variables, random processes, expectation, mean and variance, independence and conditional probability, law of large numbers, stationarity, correlation, Markov chains, central limit theorem, etc.

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Inference: maximum likelihood and Bayesian estimation, regression, classification, clustering, Markov models and Hidden Markov models, Monte Carlo, etc.

Look at the backgrounds of the professors and supervisors in Oxford's DPhil program and you'll understand why the math/stats background is important. By the way, most of the topics above are covered in first/second year level courses.

(edited 8 months ago)

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