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machine PhD Projects, Programs & Scholarships

We have 344 machine PhD Projects, Programs & Scholarships

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  (Turing) Can we use machine learning risk prediction models in diverse settings in the healthcare system?
  Prof T Van Staa, Dr N Geifman, Dr V Palin
Application Deadline: 31 January 2019
There is considerable potential to use routinely collected electronic health records (EHRs) to estimate the risks of major clinical outcomes (such as heart attack, mortality or hospital admission) which could then be used by clinicians to target treatment.
  Machine Learning and Integrative Approaches in Immunology: Developing machine-learning and mathematical models to understand heterogeneity of response to personalised cancer immunotherapy
  Dr H Koohy, Prof V Cerundolo, Prof T Dong, Prof A Simmons
Application Deadline: 26 July 2019
Cancer immunology is an area of cancer research that is gaining tremendous momentum. However, responses to immunotherapy are heterogeneous and patient care could be substantially improved by better understanding of how and why responses to immunotherapeutic approaches vary in different patients.
  Machine learning in quantum chemistry - using machine learning to improve the results of quantum chemical calculations
  Dr JG Hill
Applications accepted all year round
Machine learning methods have seen a great deal of recent success in a number of technological fields, and these techniques from applied statistics and computer science are poised to change the fields of computational and theoretical quantum chemistry.
  Machine learning techniques for the optimisation and simulation of Metal Additive Layer Manufacturing process chains
  Dr S Bigot, Dr P Kerfriden, Dr Z Ji, Dr M Packianather
Applications accepted all year round
The aim of this PhD is to develop new data analytic tools (e.g. machine learning, data mining) to support the understanding, the optimisation and the Multi-scale and multi-physics simulation of metal Additive Layer Machining (ALM) process chains.
  Integrative Computational Biology and Machine Learning: Combining computational biology, computational chemistry, and machine learning techniques with biological big data to unravel the higher genomic code of life
  Dr A Sahakyan, Prof P McHugh
Application Deadline: 26 July 2019
In the Sahakyan Group, we strive to make computational biology maximally independent from empirical experimental data, by basing our models and predictions on genomic sequences and core biological mechanisms.
  Identification of a biomarker of sensory dysfunction for patients who have developed neuropathic pain following cancer chemotherapy using functional Magnetic Resonance Imaging (fMRI) and machine learning.
  Prof D Steele, Prof L Colvin
Applications accepted all year round
Background. Acute pain is intrinsically unpleasant and aversive but is useful as it has survival benefit. However, many people suffer from chronic treatment-resistant pain that lasts months or years, lacks survival benefit, seriously impairs quality of life and causes unnecessary suffering.
  Machine learning to detect earthquakes in energy extraction
  Dr A Nowacki, Prof D Hogg
Application Deadline: 31 January 2019
Our society depends on the safe extraction of energy from a mixture of natural resources, including heat from the ground in geothermal power production, and the exploitation of hydrocarbons.
  Machine learning to detect earthquakes in energy extraction
  Dr A Nowacki, Prof D Hogg
Application Deadline: 31 January 2019
Our society depends on the safe extraction of energy from a mixture of natural resources, including heat from the ground in geothermal power production, and the exploitation of hydrocarbons.
  Machine Learning and Cognitive Modelling Applied to Video Games
  Dr K Chen
Applications accepted all year round
Video games have been viewed as an ideal test bed for the study of AI. However, most of the academic work in this area focused on traditional board and card games where limited AI techniques have been tested.
  MRes Scholarship: Machine Learning in 3D Wound Care
  Dr R Higginson, Prof J Kenkre
Application Deadline: 4 February 2019
To view this project in full, please visit. http://kess2.ac.uk/21099-2/. Here is an exciting opportunity to apply computerised machine-learning technology to improve the automated clinical assessment of chronic skin wounds and ulcers.
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