Applications are welcomed for students wishing to undertake a PhD in
Statistics at the University of St Andrews. Full funding (fees, plus
stipend of approx. £19,162) is available for well-qualified students; we
encourage applications as soon as possible to maximize your chances of
being funded. UK, EU and other overseas students are all encouraged to
apply. New PhD students would typically start in September 2024, but
this is flexible.
Members of the Statistics Division at St Andrews are particularly active
in the fields of statistical ecology, statistical medicine & molecular
biology and statistics methodology including machine learning, Bayesian
statistical inference, bioinformatics, design of experiments, estimation
of population size, computer-intensive model fitting techniques,
smoothing methods, causal inference, statistical genetics and analysis
of clustered and censored data.
General applications from potential students interested in these areas
are welcome. In addition, we are looking for candidates for the
following specific projects; more details of these, the PhD environment
and the application process are at the following web site:
https://tinyurl.com/StAndStatsPhD2024 (pdf document).
Specific projects (supervisor in brackets):
• Distance sampling with milder assumptions (Benjamin Baer and David
Borchers)
• Statistical ‘omics on graphs (Collin Bleak and Andy Lynch)
• Quantifying Trade-Offs Between Simple and Complex Models for
Decision-Making (Fergus Chadwick and Alison Johnston)
• Identifying complex spatio-temporal biomarkers of brain diseases
(Nicolò Margaritella and Michail Papathomas)
• Stochastic modelling and inference for live-cell gene expression
time-series data to unravel the mechanisms of stem cell differentiation
(Giorgos Minas, Jochen Kursawe and Cerys Manning (U. Manchester))
• Stochastic simulation, analysis, and inference of non-linear dynamical
systems (Giorgos Minas)
• Supervised learning methods to measure information transfer in biology
(Giorgos Minas)
• Incorporating Mixture of Expert Models for Longitudinal Data with
Missing and Censoring (Elham Mirfarah)
• Propagation of uncertainty for signatures of mutational processes
(Michail Papathomas and Andy Lynch)
• Understanding the uncertainty in the decomposition of cancer gene or
protein expression data (Michail Papathomas and Andy Lynch)
• Causal inference and trial emulation for ecological observational data
(Ben Swallow and Hannah Worthington)
• Movement through space and time, realistic movement for species
abundance methods (Hannah Worthington and David Borchers)
• Exploring synergies between statistical ecology and statistical
genomics (Hannah Worthington and Andy Lynch)
• Hidden Markov models for spatially structured populations (Hannah
Worthington and Chris Sutherland)
For informal discussion about any matter related to PhDs at St Andrews
please contact Prof. Len Thomas <[log in to unmask]>.
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