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We have 140 Statistics PhD Research Projects for Non-European Students
Statistics PhD Research Projects for Non-European Students
We have 140 Statistics PhD Research Projects for Non-European Students
Multimodal Machine Learning for Healthcare
Multimodal Machine Learning for Healthcare. One fully funded UK/international PhD studentship available in the broad area of statistical machine learning, with a desirable focus on multimodal large language models, uncertainty-aware AI, and/or 3D images. Read more
Bayesian Analysis of Hawkes Processes, With Applications
Project Description. Hawkes processes are an increasingly popular tool in statistics and machine learning which are used for modelling data where events occur in bursts and clusters. Read more
Using statistical and machine learning methods to detect ChatGPT text (and other AI language models)
Project Description. The use of statistical methods to analyse literary texts is common in the field of stylometry. Two of the most important challenges are. Read more
Mathematical analysis of medium-sized epidemics resulting from reactive outbreaks response
Most theoretical results in mathematical epidemiology come from the analysis of models where parameters are assumed constant. This assumption is also often made when studying outbreaks in medium-sized populations (e.g. Read more
Machine learning for extreme scale computational imaging in radio astronomy
The Biomedical and Astronomical Signal Processing (BASP) laboratory at Heriot-Watt University Edinburgh (HWU), headed by Professor Yves Wiaux is recruiting a PhD student for research on deep learning for extreme-scale computational imaging in radio astronomy. Read more
Foundations of deep learning for scalable computational imaging
The Biomedical and Astronomical Signal Processing (BASP) laboratory at Heriot-Watt University Edinburgh (HWU), headed by Professor Yves Wiaux is recruiting a PhD student for research on foundational deep learning methodology for computational imaging in astronomy and/or medicine. Read more
Data driven approaches for nonlinear inverse problems
The project aims to develop new techniques for solving complex inverse problems that arise in various scientific fields. In many real-world applications, such as medical imaging, geophysics, and material science, we often seek to recover the hidden properties of a system from indirect and noisy measurements. Read more
Hybrid Geostatistical and Machine Learning Methods for Predicting Onchocerciasis Prevalence
Onchocerciasis, also known as river blindness, is a parasitic disease caused by Onchocerca volvulus and transmitted through the bites of black flies. Read more
Modelling salivo-surveillance for detecting population immunity, transmission dynamics and changes in vaccine effectiveness
Vaccination is a highly effective public health intervention that prevents morbidity and mortality. The UK vaccination programme evolves in response to vaccine developments and emerging evidence. Read more
Modelling the impact and cost-effectiveness of hepatitis B virus testing, diagnosis, and treatment interventions to achieve elimination in the UK
Chronic infection with hepatitis B virus (HBV) leads to serious liver-related morbidity and mortality. In the UK, although HBV prevalence is low and has been estimated to be 0.58% overall, this equates to an estimated 268,767 (95%CI. Read more
Understanding Causal Effects of Perturbations on Social Systems to Allow Prediction of Natural Populations’ Responses to Change
Natural populations are often experience a variety of perturbations, ranging from extreme seasonal changes, weather events, disease outbreaks, or anthropogenic pressures. Read more
Unveiling intercellular dynamics in cancer progression using deep learning on single-cell and spatial transcriptomics data
The project. Colorectal carcinoma (CRC) is the second deadliest cancer worldwide, with a 5-year overall survival rate below 10%. CRC is classified into four consensus molecular subtypes (CMS1–4). Read more
Machine learning-based biophysical models of antibody properties and function
Co-supervised by Prof. Mauricio Barahona https://profiles.imperial.ac.uk/m.barahona. Project summary. Antibodies are proteins that play a key role in the immune response against pathogens by binding specifically to the pathogen. Read more
Efficient design of high-throughput molecular studies.
A typical experiment might consist of taking one sample from each of 100 patients, measuring many molecular properties, and then performing an analysis. Read more
Topics arising from the UK Prostate ICGC (International Cancer Genome Consortium) project.
The UK Prostate ICGC project has generated many data on a cohort of men with the prostate cancer that have the following notable features. Read more