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Statistics PhD Projects, Programmes & Scholarships
We have 64 Statistics PhD Projects, Programmes & Scholarships
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Measuring Tail Operational Risk under Extreme Value Theory
Operational risk is defined as "the risk of direct or indirect loss resulting from inadequate or failed internal processes, people and systems or from external events'' by Basel Committee on Banking Supervision (BCBS, 2003). Read more
Finding function in the non-coding genome
One of the strongest signatures of a functional genomic region is evolutionary conservation. Read more
Evolutionary game theory and population dynamics
Evolutionary Game Theory (EGT) [MSP73] has proven to be a powerful and versatile mathematical framework for the modelling and analysis of complex biological (and economical) systems whenever there is frequency dependent selection. Read more
Numerical Algorithms and Analysis for Deterministic and Stochastic Systems
Details. Complex systems in biology, chemistry, and physics are often modelled using ordinary/partial/stochastic differential equations. Read more
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Time changed multivariate models: calibration and fit on financial data
Abstract. Multi-asset derivative pricing is still an active field of research in financial modeling, calling for multivariate stochastic models that reproduce well-known stylized facts such as skewness and excess kurtosis of marginal return distributions. Read more
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Stochastic modelling of subsurface water flow
Abstract. The subsurface water flow in natural formations with multiscale heterogeneities exhibit complex patterns. Our aim is to efficiently represent this motion by resorting to stochastic models for anomalous transport (e.g., continuous time random walks, Lévy flights, Lévy walks). Read more
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Duality methods in Bayesian inference for hidden Markov models
Abstract. The project deals with Bayesian inference on the trajectory of a diffusion process or on the parameters that characterize its drift and volatility, under the assumption of a hidden Markov model framework with noisy data collection. Read more
PhD position in Palaeogenetics
In the project, new population-genetic and biostatistical methods will be developed on the computer in order to uncover genetic structures within human populations of the past. Read more
PhD Scholarship in Population Studies / Demography: to investigate spatial and contextual aspects of migrant and ethnic minority employment, family, and housing in the UK.
We invite applications from qualified and highly motivated students for a 3-year PhD studentship to investigate spatial and contextual aspects of migrant and ethnic minority employment, family, and housing in the UK. The study will compare migrant life trajectories between different regions and/or will investigate the effect of contextual factors on migrant employment, family, and housing. Read more
Numerical Algorithms for Molecular Systems and Data Science
Details: . Stochastic differential equations (SDEs) are widely used in science and engineering in order to model systems where random effects play a significant role. Read more
PhD candidate (m/f/d) in Computational Biology
We are offering a position, available immediately, as a. PhD candidate (m/f/d) in Computational Biology. About us. The Schirmer lab is part of the ZIEL - Institute for Food & Health at the Technical University of Munich and located in Freising-Weihenstephan. Read more
Data Analytics and Sensometrics
We are seeking a candidate for a fantastic opportunity to study for a PhD in Data Analytics and Sensometrics. We are seeking a PhD candidate for a research project funded by the Riddet Institute (. Read more
Machine learning methods for the human microbiome (QUINCE_E21EPSRC)
This project based in Dr Quince’s group at the Earlham Institute (. https://www.earlham.ac.uk/quince-group). will use machine learning to develop new computational methods for the integrated analysis of metagenomics and metabolomics data from the human microbiome. Read more
Machine Learning Meets Sequential Monte Carlo Methods
This project lies on the intersection of modern machine learning techniques and sequential Monte Carlo methods. Sequential Monte Carlo (SMC) methods, e.g. Read more
Data integration and hypothesis testing for brain tumour multi-omics data
This project aims to develop computational and statistical methods for the integrated learning of biological networks from multiple sources to further our understanding of Glioblastoma Multiforme (GBM), a highly aggressive primary brain tumour for which there is currently no cure. Read more