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Statistics PhD Projects, Programmes & Scholarships
We have 163 Statistics PhD Projects, Programmes & Scholarships
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Long COVID: identification of persons at risk and for treatment stratification via artificial intelligence methodology
Long-term effects of coronavirus (long COVID) have become the burden of many people across the globe, affecting their everyday life and work performance. Read more
Physiological basis linking sleep continuity and age-related cognitive deficits: in vivo manipulation and monitoring of activity of layer 5 neurons in the cortex of mice
Research Group:
Surrey Sleep Research Centre (SSRC)
A good night’s sleep is essential to maintain proper mental health. In particular, consolidated periods of sleep (i.e. sleep continuity) seem to be critical for cognition as reported in humans (Della Monica et al., 2018) and rodents (Rolls et al., 2011). Read more
Advanced data-driven prognostics for improved operation and maintenance recommendations
EngD in the new EPSRC Centre for Doctoral Training in Wind and Marine Energy Systems and Structures at the University of Strathclyde collaborating with Renewable Energy Systems (RES). Read more
Improving cost-effectiveness of clinical trials in frailty with in silico predictions
Dunhill Medical Trust and Healthy Lifespan Institute Doctoral Training Programme Studentship. This three and a half (3.5) year studentship is part of the newly formed Dunhill Medical Trus. Read more
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
The use of Artificial Intelligence Approaches to the Analysis of Heart Rate Variability Data (Advert Ref: SF22/EE/CIS/SICE)
Importance. Heart rate variability (HRV), or the variation in the time interval between consecutive heartbeats, is a proven measure for assessing changes in Autonomic Nervous System(ANS) activity. Read more
Low-Carbon Policy Evaluation for Manufacturing Supply Chains using Causal Machine Learning (Advert Ref: SF22/EE/CIS/HU)
The recent disruptions of oil and gas supply in the UK and around the world are crippling industrial manufacturers that turn heavy fossil energy consumptions to semi-raw materials and parts. Read more
Memory in vegetation response to environmental fluctuations (Advert Reference: RDF22-R/EE/GES/LIU)
Plants live in and respond to fluctuating environments around them. Importantly, many plant responses are not only driven by current environmental conditions, but show “memory” to conditions in the past. Read more
Computational modelling of composite plates and shells using data-driven approach (Advert Reference: RDF22-R/EE/MCE/NGUYEN)
Recent report from Department for Business, Energy & Industrial Strategy and Innovate UK in July 2021 revealed that the UK composite end-product market in 2019 was estimated at £2.8B and expected to grow to £4.3B by 2035. 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