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Statistics PhD Projects, Programs & Scholarships in Manchester

We have 58 Statistics PhD Projects, Programs & Scholarships in Manchester

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  Integrative statistical inference methods for eukaryotic gene regulation with applications to embryonic stem cell differentiation
  Dr I Iqbal, Prof M Rattray, Prof A Sharrocks
Applications accepted all year round

Funding Type

PhD Type

Embryonic stem cells (ESCs) can differentiate into different cell types through intermediary cell states and deeper understanding of the regulatory control underlying these differentiation stages is a very important topic in the study of mammalian development (Yang et al., 2014 & 2019).
  (MRC DTP) Statistical machine learning approaches cross-cohort multi-omics data
  Prof M Rattray, Dr C Yau
Application Deadline: 15 November 2019

Funding Type

PhD Type

Multimorbidity refers to the co-existence of two or more chronic conditions in an individual and its prevalence is increasing due to aging populations.
  (MRC DTP) Incorporating causal inference in clinical prediction models
  Dr M Sperrin, Dr G P Martin, Dr H Guo
Application Deadline: 15 November 2019

Funding Type

PhD Type

Clinical prediction models (CPMs) take what we know about a person and predict the probability of subsequent outcomes using a regression model or algorithm [1].
  (MRC DTP) Development of a software system to automatically detect and quantify foot collapse
  Dr Claudia Lindner, Prof T Cootes
Application Deadline: 15 November 2019

Funding Type

PhD Type

Charcot arthropathy is collapse of a joint secondary to peripheral neuropathy, most commonly secondary to diabetes or alcoholism.
  (MRC DTP) Understanding the mechanisms responsible for aberrant atrial calcium handling in heart failure
  Dr K Dibb, Prof A Trafford, Dr C Pinali
Application Deadline: 15 November 2019

Funding Type

PhD Type

Atrial fibrillation (AF) is the most common cardiac arrhythmia, expected to affect ~18 million Europeans by 2060. AF often occurs in patients with heart failure (HF), increasing their risk of death by ~2 fold.
  (MRC DTP) Computer-aided Detection of Osteoporotic Vertebral Fractures in Clinical Images Using Convolutional Neural Network Constrained Local Models (CNN-CLMs)
  Dr P Bromiley, Prof T Cootes, Dr EP Kariki
Application Deadline: 15 November 2019

Funding Type

PhD Type

Osteoporosis is a common, degenerative skeletal disorder that increases the risk of fractures and causes significant morbidity and mortality.
  (MRC DTP) Quantifying change in mammographic appearance using Artificial Intelligence
  Dr S Astley, Dr Yit Lim, Dr S Howell, Dr M Fergie
Application Deadline: 15 November 2019

Funding Type

PhD Type

Breast cancer is the most common cancer among women, with over 54,000 new cases diagnosed each year in the UK.
  (MRC DTP) Longitudinal multi-omics analysis to identify signatures of clinical, imaging and molecular remission in the treatment stratification of new onset, treatment-naïve, early rheumatoid arthritis
  Prof Maya Buch, Dr D Plant, Prof A Morris
Application Deadline: 15 November 2019

Funding Type

PhD Type

Rheumatoid Arthritis (RA) is a complex and heterogeneous syndrome, yet we manage all patients similarly. Treatment cycling and failure to targeted therapies have emerged as key clinical challenges.
  (MRC DTP) Predicting failure of methotrexate therapy in rheumatoid arthritis patients: statistical approaches to multiple outcomes and repeated observations
  Dr J Sergeant, Dr G P Martin, Prof K Hyrich, Dr S Verstappen
Application Deadline: 15 November 2019

Funding Type

PhD Type

Most patients with rheumatoid arthritis begin treatment with the drug methotrexate (MTX). MTX is cheap and is effective for many patients, but does not work for everyone.
  (MRC DTP) Understanding the biophysical basis of functional Magnetic Resonance Spectroscopy measurements in the human brain
  Dr L Parkes, Dr M Montemurro, Dr C Lea-Carnall
Application Deadline: 15 November 2019

Funding Type

PhD Type

Magnetic resonance spectroscopy (MRS) is a valuable tool used to study the chemical composition of the human brain.
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