University of Huddersfield - Computer Science and Informatics PhD

University of Huddersfield

Computer Science and Informatics PhD

A PhD is the highest academic award for which a student can be registered. This programme allows you to explore and pursue a research project built around a substantial piece of work, which has to show evidence of original contribution to knowledge.

A full-time PhD is a programme of research culminating in the production of a large-scale piece of written work in the form of a research thesis that should not normally exceed 80,000 words (excluding ancillary data).

Completing a PhD can give you a great sense of personal achievement and help you develop a high level of transferable skills which will be useful in your subsequent career, as well as contributing to the development of knowledge in your chosen field.

You are expected to work to an approved programme of work including appropriate programmes of postgraduate study (which may be drawn from parts of existing postgraduate courses, final year degree programmes, conferences, seminars, masterclasses, guided reading or a combination of study methods).

You will be appointed a main supervisor who will normally be part of a supervisory team, comprising up to three members to advise and support you on your project.

Entry Requirements

The normal level of attainment required for entry is:

  • A Master’s degree or an honours degree (2:1 or above) or equivalent, in a discipline appropriate to the proposed programme to be followed, or appropriate research or professional experience at postgraduate level, which has resulted in published work, written reports or other appropriate evidence of accomplishment.

If your first language is not English, you will need to meet the minimum requirements of an English Language qualification. The minimum for IELTS is 6.0 overall with no element lower than 5.5, or equivalent will be considered acceptable.

Course Details

There are several research topics available for this degree. See below examples of research areas including an outline of the topics, the supervisor, funding information and eligibility criteria:

Innovative Learning Techniques for AI Planning.
3D Imaging and Crime Scene Reconstruction
A Multimodal Approach to the in-vivo Measurement of Bones and Joints Kinematics in Real-time
A collaborative visual analytics platform for building improved quality smart buildings
ATLAS at CERN: Trigger and data aquisition (TDAQ) system
An investigation into the deep learning approach in information analysis using graph-based theories
Applying Deep Learning for Intrusion Detection System in the Internet of Things (IoT) Network
Automatic analysis of medical notes
Blockchain trust mechanisms for the Industrial Internet of Things
CORE: Crowdsourcing an Ontology of Requirements Engineering approaches
Combining data mining techniques and machine learning algorithms for air pollution prediction.
Cross-Realm Analytics for Business Intelligence [2]
Dead-Zone or Nearly-Dead-Zone Finder in Large IoT Networks
Enabling Smart Healthcare at Home in Fog Computing
Enhancing Interpretability of Machine Learning Models
Explainable predictive analytics using an ontologically based feature space
Fostering mental health through technology-enhanced non-pharmacological approaches
Future Cities: Design and development of an intelligent web-based visual analytics platform
Governing distributed learning algorithms within Internet of Things (IoT) networks
Graph-based Security Evaluation & Design in the Internet of Things
High-Performance Computing Security Framework
Hybrid rule-based and data-driven approaches to activity recognition in a smart home environment
In-Transit Analytics of data streams from Internet of Things (loT) devices
Induction of Intelligent and Interpretable Systems for Clinical Decision Support
Innovative Big Data Analytics in Space Science: Insights of Big Data – Big Data Fuelling Big Data
Innovative Learning Techniques for Improving the Performance of Al Planning Engines.
Learning analytics and personalised learning: a game-based framework
Light-Weight Software Testing Framework
Machine Learning of Domain Models for Long Term Autonomy and Explainable Al.
Machine Learning techniques applied to identify significant gene biomarkers for efficient cancer treatment.
Machine Learning to Guide Malaria Diagnostics and Treatment.
Machine Learning using human-derived knowledge for machine tool maintenance
Mathematical Modelling of Brewing Coffee
Mathematical Modelling of Instabilities in Two Phase Flows.
Metrics for Sustainable Scientific Software
Microservice Performance Degradation Correlation
Monolithic Systems to Microservice Architectures
Multi-inhabitant activity recognition and indoor localisation in a smart home environment
Multiagent Systems for Resilient Internet of Things (IoT) Architectures
Optimising the processing of smart-house data across edge and central devices
Personalised student mental state detection in an indoor learning environment using wearable and ambient sensors
Quantum Software Architectures
Robust Spatial and Geometric Features Extraction from Biometric Images
Secure Multiparty Authentication for the Internet of Things
Secure software by design from an adversary perspective
Security and Privacy Preservation in the Internet of Things
Semantic Digital Twins
Semantic and Knowledge Technologies for the Internet of Things.
Software Architectural Reconstruction and Recovery (ARR)
Software Architecture Consistency
Software Architecture Metrics and Measurements
Software Architectures for Smart Monitoring of the Ocean
Stable Computer Simulation of Railway Ballast using Discrete Particles

*The information’s are correct at the time of publishing, however it may change if university makes any changes after we have published the information. While we try our best to provide correct information, It is advisable to call us or visit university website for up to date information.

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