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prediction PhD Projects, Programs & Scholarships

We have 126 prediction PhD Projects, Programs & Scholarships

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  Crystal structure prediction based NMR crystallography
  Dr G Day
Applications accepted all year round

Funding Type

PhD Type

Project description. A fully funded 4 year PhD studentship is available in computational materials chemistry at the University of Southampton.
  Artificial intelligence and machine learning for clinical prediction: Evaluating design, conduct, reporting and 'spin' #NDORMS-2020/3
  Prof G Collins, Dr M Schlussel, Dr P Dhiman, Dr J de Beyer
Application Deadline: 10 January 2020

Funding Type

PhD Type

1. Professor Gary Collins. 2. Dr Michael Schlussel. 3. Dr Paula Dhiman. 4. Dr Jennifer de Beyer. Artificial intelligence (AI) and machine learning (ML) are increasingly seen as solutions to many healthcare problems (e.g., for risk prediction, imaging), and the pace of development is showing no signs abating.
  Dynamic prediction modelling in Parkinson’s disease
  Dr D McLernon, Dr A Macleod
Applications accepted all year round

Funding Type

PhD Type

1. Background to the project. Parkinson’s disease (PD) is a progressive, disabling, neurodegenerative disorder which is common in the elderly.
  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.
  Dynamic prediction of in-patient mortality based on electronic health record data: a comparison of landmarking and machine learning approaches
  Dr S Kiddle, Dr J Barrett
Application Deadline: 7 January 2020

Funding Type

PhD Type

Background. There is great potential to use electronic health record (EHR) datasets to improve care of patients, as EHR are typically bigger, longer and more representative of the healthcare population than traditional research cohorts.
  Modelling Creep-Fatigue Damage and Life Prediction for Gas Turbine Rotors
  Prof W Sun
Applications accepted all year round

Funding Type

PhD Type

The current market conditions are such that combined cycle gas turbine (CCGT) plants are now considering double two-shift operation, so potentially accruing upwards of 600 starts per year.
  Advancing personalised care: treatment selection and outcome prediction research
  Dr J Delgadillo, Prof M Barkham
Applications accepted all year round

Funding Type

PhD Type

Personalized medicine, defined as “the use of marker-assisted diagnosis and targeted therapies” (Ginsburg & McCarthy, 2001), has enhanced health and preventive care for conditions such as cardiovascular problems, cancer and osteoporosis.
  Prediction of Service Demand and Quality of Experience using Deep Learning for Healthcare
  Dr Y Zhang, Prof H Tianfield
Applications accepted all year round

Funding Type

PhD Type

SCEBE-19-010. With the explosive growth of services on the Internet, the number of choices is overwhelming. Traditionally services are passively searched by users.
  Exploiting advanced methods for protein structure prediction
  Dr D J Rigden, Dr R Keegan
Applications accepted all year round

Funding Type

PhD Type

Protein structural information is crucial for an understanding of protein function and evolution. Currently, there is only experimental data for a tiny fraction of the protein universe.
  Longitudinal trajectories of renal function, and dynamic prediction of progression to end-stage renal disease: a data-driven analysis and validation using regression and machine learning methods in a bi-national population-based cohort #NDORMS-2020/1
  Prof D Prieto-Alhambra, Dr V Strauss, Dr S Khalid, Dr D Robinson
Application Deadline: 10 January 2020

Funding Type

PhD Type

1. Prof Daniel Prieto-Alhambra. 2. Dr V Strauss. 3. Dr Sara Khalid. 4. Dr Danielle Robinson. 5. Dr Laurie Tomlinson. There is a scarcity of data on the natural history of chronic kidney disease (CKD) in the general population, and on markers of rapid progression to end-stage renal disease (ESRD).
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