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Data Analysis PhD Projects, Programs & Scholarships in Bradford

We have 10 Data Analysis PhD Projects, Programs & Scholarships in Bradford

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  Centre for Doctoral Training in Data Analytics and Society
This is an exciting opportunity to undertake a 4-year funded integrated PhD and MSc in Data Analytics and Society, working on a project which has been developed in conjunction with external organisations.
  Health Opportunity for Patient Empowerment in Gestational Diabetes (HOPE-GDM)
  Research Group: Chemistry and Biosciences
  Prof A M Graham, Prof R Qahwaji, Prof A Locke
Applications accepted all year round
Gestational Diabetes is a temporary condition diagnosed at 24-28 weeks gestation, where the body is unable to maintain normal blood glucose levels due to insulin resistance during pregnancy.
  Genomic basis of extra-group paternity in the cooperatively breeding Seychelles warbler
  Dr H L Dugdale
Applications accepted all year round
Indirect genetic benefits are hypothesised to drive the evolution of extra-group paternity (EGP), yet its genomic basis is unknown.
  Development of Computer Vision System for Remote sensing and processing of satellite images
  Research Group: Visual Computing
  Prof R Qahwaji
Applications accepted all year round
Remote sensing applications require the processing of satellite or drones images to detect regions of interest, and develop a variety of applications such as prediction of natural hazards, tracking of certain events, processing and visualisation of big data sets, etc.
  Development of Digital Health Platforms for processing patients’ data using Artificial Intelligence and visual computing Technologies
  Research Group: Visual Computing
  Prof R Qahwaji
Applications accepted all year round
There have been considerable advances in the Internet of Things (IoT) and wearable sensors in recent years, and the arrival of the 5G wireless spectrum will provide the capacity for new sensor platforms and devices to capture and share data reliably between devices and healthcare providers.
  Evolutionary algorithms for engineering applications: developing computational methods and optimisation frameworks to solve engineering optimisation problems
  Research Group: Mechanical and Process Engineering
  Dr JP Li
Applications accepted all year round
This highly multidisciplinary PhD project will develop computational methods and optimisation frameworks to solve engineering optimisation problems.
  Species Conservation Evolutionary Algorithms for multi-objective optimisation and applications in Engineering
  Research Group: Mechanical and Process Engineering
  Dr JP Li
Applications accepted all year round
This highly multidisciplinary PhD project will develop computational methods or optimisation frameworks to solve engineering optimisation problems.
  Planning and Design of Distribution Networks with Integration of Smart Grid Technologies
  Research Group: Communication and Networks
  Dr G Mokryani
Applications accepted all year round
The UK Government’s target is to achieve 15% of electricity generation from renewable energy sources (RES) by 2020. The installation of large amounts of RES in distribution networks introduces several economic and technical challenges to distribution network operators (DNOs).
  Development of a Model Based Systems Engineering Framework / Ecosystem to Support “Resilience Engineering” for Complex Automotive Systems
  Research Group: Automotive Research Centre‌
  Prof F Campean
Applications accepted all year round
The design paradigm for automotive systems faces significant challenges from the evolution towards the vision of the future car as a mobile smart device that is substantively defined by its symbiotic interaction and integration with its environment, supporting increased levels of autonomy as well as user-centric service innovation.
  Intelligent Big Data Analytics for Manufacturing and Engineering Applications
  Research Group: Applied Computing
  Dr S Konur
Applications accepted all year round
Information processing and understanding using AI and machine learning is seen as a future growth direction to deal with increasingly large and complex data-sets, making sense of ambiguity in data that is often noisy and not always robust.
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