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Computer Science & IT (medical statistics) PhD Projects, Programs & Scholarships

We have 15 Computer Science & IT (medical statistics) PhD Projects, Programs & Scholarships

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  Optimising the efficiency of systematic reviews of diagnostic test accuracy studies
  Dr P Whiting
Application Deadline: 25 November 2019

Funding Type

PhD Type

Diagnostic test accuracy (DTA) systematic reviews (SR) provide the most reliable form of evidence for decision makers on the accuracy of a diagnostic test.(1) A full SR is extremely resource intensive, often taking months or years to complete.
  Network Inference and Machine Learning: Identification of Diseases-gene Regulation
  Dr F He
Application Deadline: 31 January 2020

Funding Type

PhD Type

Coventry University is inviting applications from suitably-qualified graduates for a fully-funded PhD studentship. The successful candidate will join the project ‘Network inference and machine learning.
  MRC DiMeN Doctoral Training Partnership: Building integrated machine learning and genomic approaches to quantitate bone quality and fracture risk
  Prof M Wilkinson, Prof A Frangi, Prof E Zeggini
Application Deadline: 6 January 2020

Funding Type

PhD Type

Is this project for you?. This PhD Project would suit a student inspired by the prospect of participating in a truly multidisciplinary team.
  PhD opportunities at the UKRI Centre Artificial Intelligence for Healthcare

Funding Type

PhD Type

Our vision is to train PhD students to become next-generation innovators in Artificial Intelligence (AI) applied to Healthcare, and we are inviting excellent, suitably qualified graduates with strongest AI-related skills and strong interest in healthcare.
  Modelling the impact of interventions in atrial fibrillation, the commonest cardiac rhythm disorder and a global healthcare problem
  Dr G Czanner, Dr I Olier, Prof P Lisboa, Prof G Lip
Applications accepted all year round

Funding Type

PhD Type

Project description. It is known that the risk of developing CVD is increasing in the UK along with the rest of the World. It is believed that the culprits are lifestyle and environmental factors.
  Statistical and machine learning methods for risk prediction of cardiovascular diseases from complex longitudinal data
  Dr G Czanner, Dr I Olier, Prof P Lisboa, Prof G Lip
Applications accepted all year round

Funding Type

PhD Type

Project description. Risk prediction models are used in clinical decision making and are used to help patients make an informed choice about their treatment.
  (BHF Acc) Multi-parametric MR imaging of myocardial fibrosis, perfusion and oxygenation
  Dr J Naish, Dr Chris Miller, Prof T Cootes, Dr E Johnstone
Application Deadline: 13 December 2019

Funding Type

PhD Type

The BHF Manchester Centre for Heart and Lung Magnetic Resonance Research (MCMR) is a new £3.1M investment in cardiovascular MR (CMR) aimed at translating multidisciplinary research excellence in Manchester into cardiovascular patient benefit.
  Digital manikins as a novel tool to measure self-reported pain in large-scale research studies
  Dr S Van der Veer, Prof W Dixon
Applications accepted all year round

Funding Type

PhD Type

Moderate to severe pain affects 1 in 5 adults. It is among the most prominent symptoms of musculoskeletal disease, and substantially affects the quality of life of people suffering from it.
  Establishing the reliability and validity of digital tools to collect patient generated data
  Dr J McBeth, Prof W Dixon
Applications accepted all year round

Funding Type

PhD Type

Background. The rise in consumer electronics ownership, such as smartphones and smartwatches, offers unique opportunities to collect granular subject-reported and passively collected research data via remote monitoring.
  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).
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