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machine learning PhD Projects, Programs & Scholarships

We have 333 machine learning PhD Projects, Programs & Scholarships

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  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.
  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.
  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.
  Integrating structured knowledge with Machine Learning Representations
  Dr F Guerin, Dr C Lin
Applications accepted all year round
he problem. Machine learning systems are effective at learning from scratch, but it is not clear how to reuse the knowledge they have learned for another purpose.
  Intelligent Automated Interpretation and Reporting of Medical Images
  Dr E Chung, Dr M Zare
Application Deadline: 3 April 2019
Over the last decade, the quantity of digital medical imaging data held by NHS hospitals and other healthcare establishments has increased dramatically.
  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.
  Rapid additive and solvent screening for morphology control in continuous crystallisation via machine learning
  Dr B Castro Dominguez, Prof C C Wilson
Application Deadline: 24 March 2019
The Centre for Sustainable Chemical Technologies (CSCT) at the University of Bath focuses on developing new molecules, materials, processes and systems from the lab right through to industrial application, with an emphasis on practical sustainability.
  Self-Learning Intrusion Detection System Through Machine Learning (Advert Reference: SF18/CIS/ISSAC)
  Dr B Isaac
Applications accepted all year round
As network applications are increasingly used with the popularity of broadband networks and Internet of Things (IoT), network security is a very important issue.
  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.
  Machine Learning-Enhanced Modelling and Simulation of Subsurface Reservoirs
  Research Group: Institute of Petroleum Engineering
  Dr A ElSheikh
Application Deadline: 15 April 2019
The School of Energy, Geoscience, Infrastructure and Society at Heriot-Watt University (HWU), is looking for a PhD candidate to work on an industrially funded project at the interface of machine learning and subsurface flow modelling.
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