Computer Science and Informatics (PhD) 2017-18

This course also available for 2016-17 entryThis course also available for 2018-19 entry

The Research Degree

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 40,000 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.


Start date:
This research degree has multiple possible start dates including:
18 / 09 / 2017
08 / 01 / 2018
16 / 04 / 2018

Your start date may be decided in agreement with your supervisor.

Duration:

The maximum duration for a full time PhD is 3 years (36 months) with an optional submission pending (writing up period) of 12 months.

Sometimes it may be possible to mix periods of both full-time and part-time study.

Most students commence their studies in October at the beginning of each academic year.

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.

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

Further information on international entry requirements and English language entry requirements is available on our international webpages

Contact:

Tel: +44 (0) 1484 473969
Email: researchdegrees@hud.ac.uk

Places available:
(this number may be subject to change)

Location:
Huddersfield, HD1 3DH

Apply now Book on an Open Day or Study Fair Order a prospectus Ask a question

What can I research?

Research topics available for this degree:

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

Research titleSupervisorsApply
3D Imaging and Crime Scene Reconstruction
Outline
Crime scene reconstruction is a forensic science discipline in which one gains explicit knowledge of the series of events that surround the commission of a crime using deductive and inductive reasoning physical evidence scientific methods and their interrelationships. This programme aims at investigating innovative forensic imaging techniques for producing accurate reproduction of a crime scene or an accident scene for the benefit of a court or to aid in an investigation. The programme will start from reviewing the state-of-the-art of 3D imaging techniques such as Augmented Reality and stereoscopy for creating or enhancing the illusion of depth in an image. The research will then propose innovative 3D imaging approaches based on photogrammetry theories and recent developments in remote sensing technologies for the acquisition and understanding of accurate and reliable measurements of a diverse range of natural and manmade structures including underground disturbances. The research encompasses scientific disciplines including image networks and sequences vision metrology laser scanning and range imaging as well as 3D modelling and interactive visualisation. The research output is anticipated to benefit forensic applications such as stockpile monitoring and underground abnormality detection.
Eligibility
The standard entry requirement for PhD study is a first or upper second-class honours degree, or the overseas equivalent, in a relevant subject. In certain circumstances, a lower second-class honours degree supplemented by a master’s degree, or appropriate relevant work experience, may be acceptable.
Funding

Please see our Scholarships page to find out about funding or studentship options available.

Deadline
Standard University deadlines apply: http://www.hud.ac.uk/researchdegrees/howtoapply/
How to apply
Analysis and Process of RGB-D Data for Emerging Applications
Outline
The availability of cheap RGB-0 sensors has led to an explosion over the last five years in the capture and application of colour plus depth. This resulted in a demand for robust and real-time processing of data that is typically noisy and incomplete. This project will investigate the state-of-the-art and key techniques in enabling real-time segmentation and feature learning from RGB-0 data, saliency-based approaches to 20/RGB-O registration, tracking and fusion from multiple sensors, point-cloud processing and 30 reconstructions, illumination and realistic rendering, robust RGB-0 SLAM, etc. Novel computational and visualisation models and techniques will be devised to facilitate applications such as in digital heritage, action and activity recognition, occluded 30 scene reconstruction, 40 modelling of dynamic shapes, and VR/AR system integration.
Eligibility
The standard entry requirement for PhD study is a first or upper second-class honours degree, or the overseas equivalent, in a relevant subject. In certain circumstances, a lower second-class honours degree supplemented by a master’s degree, or appropriate relevant work experience, may be acceptable.
Funding
Please see our Scholarships page to find out about funding or studentship options available.
Deadline
Standard University deadlines Apply: http://www.hud.ac.uk/researchdegrees/howtoapply
How to apply
Automated planning with non-linear numeric domains
Outline
Automated planners (i.e. software capable of choosing and organising actions by anticipating their expected outcome) are being increasingly used to automate real-world planning problems. For example, logistic problems, traffic management, and in planning for engineering processes. Although current automated planners are capable of solving a problems with a large array of characteristics (temporal, numeric, etc.), they struggle to plan for domains which are numerically rich by nature (e.g. containing non-linear resources). This often results in the requirement for in-depth knowledge to model real-world domains, and can even result in the technology been deemed unsuitable for the application. This project will investigate how current state-of-the-art in automated planning theory can be further developed to overcome this limitation.
Eligibility
The standard entry requirement for PhD study is a first or upper second-class honours degree, or the overseas equivalent, in a relevant subject. In certain circumstances, a lower second-class honours degree supplemented by a master’s degree, or appropriate relevant work experience, may be acceptable.
Funding

Please see our Scholarships page to find out about funding or studentship options available.

Deadline
Standard University deadlines apply: http://www.hud.ac.uk/research/howtoapply/
How to apply
Automated Video Retrieval and Deep Neural Network-based Scene Description
Outline
This project will investigate effective models and techniques to automatically analyse, retrieve, and describe video files and its contents for security and surveillance applications. It is anticipated that the research findings will facilitate the effort in content analysis, video archive management, and video annotation within the context of automated situation awareness, diagnosis and decision support. The research will focus on the extraction of structured knowledge from large image/video collections recorded over networks of cameras and Closed-circuit Television systems (CCTVs) deployed in real sites. Latest natural language processing (NLP) theories and their related computational linguistic practices, for example, Deep Recurrent Neural Network (RNN) will be evaluated for automatically interpreting key contextual information contained within a video scene, such as a crowd abnormality.
Eligibility
The standard entry requirement for PhD study is a first or upper second-class honours degree, or the overseas equivalent, in a relevant subject. In certain circumstances, a lower second-class honours degree supplemented by a master’s degree, or appropriate relevant work experience, may be acceptable
Funding
Please see our Scholarships page to find out about funding or studentship options available.
Deadline
Standard University deadlines Apply: http://www.hud.ac.uk/researchdegrees/howtoapply/
How to apply
Automatic analysis of medical notes
Outline
The aim of this project is to use natural language processing and information retrieval techniques to extract relevant information from medical notes. This will entail developing a thorough understanding of existing methods and tools, testing them on concrete collections of medical notes (e.g. in mental health) and developing novel methods improving on the state of the art. In some cases, medical texts will be semi-structured, thus making analysis easier, but in other cases text will be free, which poses the biggest challenge. Analysis work may have to be carried out in collaboration with medical experts who will assess the validity and usefulness of the extracted knowledge. The successful candidate will have thorough computer science education, and will have some specialised knowledge in artificial intelligence, natural language processing or information retrieval.
Eligibility
The standard entry requirement for PhD study is a first or upper second-class honours degree, or the overseas equivalent, in a relevant subject. In certain circumstances, a lower second-class honours degree supplemented by a master’s degree, or appropriate relevant work experience, may be acceptable.
Funding
Please see our Scholarships page to find out about funding or studentship options available.
Deadline
Standard University deadlines Apply: http://www.hud.ac.uk/researchdegrees/howtoapply/
How to apply
Automatic Configuration of Answer Set Programming Solvers based on Program Transformations and Solver Parameters
Outline
Answer Set Programming is a logic-programming-based declarative formalism, which has seen successful applications in both academia and industry, not only but also because of the availability of efficient solvers. This project is based on two observations: 1. Solvers possess a significant amount of parameters, and a good choice of the parameters for a given input is far from obvious; 2. Problems to be solved can be represented in many equivalent ways as logic programs, and the choice of the representation can significantly affect solver efficiency. Observation 1 gave rise to compound systems, which select suitable parameters for a given input program often based on a decision model based on machine learning. In this project the approach will be extended to observation 2, in the sense that not only suitable parameters but also a suitable version of the input program is to be chosen by the decision model. Previous work in planning and setting proved very promising, even though the equivalences were on a purely syntactic level. The project will create both foundations and implementations of these ideas and will evaluate the findings empirically.
Eligibility
The standard entry requirement for PhD study is a first or upper second-class honours degree, or the overseas equivalent, in a relevant subject. In certain circumstances, a lower second-class honours degree supplemented by a master’s degree, or appropriate relevant work experience, may be acceptable.
Funding
Please see our Scholarships page to find out about funding or studentship options available.
Deadline
Standard University deadlines Apply: http://www.hud.ac.uk/researchdegrees/howtoapply/
How to apply
Cognitive Architecture-Controlled Humanoid Robot (CACHR) project
Outline
The CACHR project seeks to advance the fields of cognitive robotics and embodied cognitive science by developing a robot control system based upon an empirically grounded computational model of the human mind – a cognitive architecture (CA). The characteristics of human cognition, perception, locomotion and sensorimotor control (e.g., slow, stochastic, predominantly parallel, memory-based processing, attentional bottlenecks, automatic and constant learning, sub-optimal satisficing, forgetting etc.) are very different from AI mechanisms. It is an important question – not only for robotics but for cognitive science more broadly – to understand how these properties enable successful intelligent human behaviour and whether they may be used to support the same behaviour in robots. The project will require a number of technical and theoretical problems to be solved. The primary challenge will be to develop an interface between the NAO robot and the ACT-R cognitive architecture (via the ROS middleware) to translate between the symbolic knowledge representations used by the CA and the data created and used by the robot. When the initial development phase is complete, the effectiveness of the system will be tested in a series of experiments to investigate the robot's cognition and behaviour under the constrained control of the cognitive architecture. The project represents an ambitious, cutting edge conception that will allow a range of important technical and theoretical questions to be addressed concerning the benefits of applying the defining features of human cognition to the design of robots with general intelligence and also the challenges of requiring a cognitive architecture to deal with the real-world constraints imposed by embodiment. The project aligns well with UK Government, European Commission, University, and School priorities relating to Robotics and Autonomous Systems (RAS) research and represents a novel and highly innovative multidisciplinary and cross-school approach to cognitive robotics that will provide a dedicated, long-term research application for the new NAO robot, and integrate PARK's existing expertise in AI methods with broader cognitive science theories and approaches. As such, the project will be of interest to other academic and industrial researchers working in cognitive and developmental robotics, for example, the developmental robotics research lab at Aldebaran Robotics. To support the PhD student's experience and skill set on knowledge transfer and R&D in the robotics industry, contact will be made with Aldebaran (and other appropriate institutions e.g., the Centre for Robotics and Neural Systems at Plymouth University) to establish research links and propose visits.
Eligibility
The standard entry requirement for PhD study is a first or upper second-class honours degree, or the overseas equivalent, in a relevant subject. In certain circumstances, a lower second-class honours degree supplemented by a master’s degree, or appropriate relevant work experience, may be acceptable.
Funding

Please see our Scholarships page to find out about funding or studentship options available.

Deadline
Standard University deadlines Apply: http://www.hud.ac.uk/researchdegrees/howtoapply/
How to apply
Declarative Specification of Heuristics for Answer Set Programming
Outline
Answer Set Programming (ASP) is a declarative programming paradigm. This means that a problem to be solved is specified in a mathematically precise way, by describing its solutions. In the case of ASP this is done by means of rules. The solutions are then calculated by means of a general purpose algorithm. This allows for an easy update of the program description, however it is not easy to incorporate domain knowledge that may be available for finding solutions more effectively (heuristics). The project should study and design a means for specifying heuristics in a declarative way for ASP. There are a few attempts on this subject, but they have various limitations, most notably involving the grounding step present in the state of the art ASP solvers. In the course of the project a prototype system should be built and evaluated both qualitatively (how easy is it to specify heuristics) and quantitatively (how effective are the specified heuristics).
Eligibility
The standard entry requirement for PhD study is a first or upper second-class honours degree, or the overseas equivalent, in a relevant subject. In certain circumstances, a lower second-class honours degree supplemented by a master’s degree, or appropriate relevant work experience, may be acceptable.
Funding

Please see our Scholarships page to find out about funding or studentship options available.

Deadline
Standard University deadlines Apply: http://www.hud.ac.uk/researchdegrees/howtoapply/
How to apply
Education in a complex world: fostering learning in formal and informal environments through complexity science, information technologies, and game-based interaction design
Outline
We live in a complex world, defined by dynamics of constant change, and characterised by unpredictability, unknowability and uncontrollability. The inability to cope with complexity leads to succumbing to wicked problems, with tragic consequences on human development. “Complexivist” mindsets are required to cope with complexity. In order to prepare individuals for an increasingly complex and intertwined world, contemporary education should foster the ability to adapt to change, to understand phenomena in context, to face ill-defined situations and to work in collaboration with others who may not share ideas or interests. Furthermore, deep learning should be supported and facilitated also in informal environments, where engagement with complexity can be higher and deeper. This project studies the domain of learning, with the aim of developing novel adaptive educational strategies and instruments to leverage complexity science, information technologies and game-based interaction design techniques to: • Promote learners’ engagement with complex problems • Foster learning and the development of “complexivist” mindsets in formal and informal environments.
Funding

Please see our Scholarships page to find out about funding or studentship options available.

Deadline
Standard University deadlines Apply: http://www.hud.ac.uk/researchdegrees/howtoapply/
How to apply
Forensic Evidence Digitisation and Visualisation
Outline
The so-called “Computer Forensic” research to-date seemingly only focus on processing evidences directly “recorded” from computing devices and had fallen short of integrating other forms and sources of the “digitised” evidences, including genetic analysis results from forensic labs, observed or extracted crime patterns, for making a comprehensive understanding through correlation. In this programme, digital forensic evidences (DFEs) are considered being formed by all digitised legal items (or their digital access index), and not just the computer-generated evidences. The proposed work aims at investigating an effective “Story-Model” visualization and validation methods for assisting police investigation and jury decision making based on the DFEs. The main objectives can be summarised as: to investigate the state-of-the-art in effective processing of large quantities of DFE data, and to establish the theoretical foundation and architecture for DFE presentation in familiar visual forms that can facilitate efficient insight gaining into legal evidences. The development of a spatial-temporal volume (STV) framework for digital forensic evidence (DFE) definition, encapsulation, and validation. It will allow the storage, retrieval, visualisation, and analysis of all key items of a legal proceeding to be performed under a unified frame of references along the dimensions of event/evidence timelines and their spatial distributions. The deployment of information resulted from forensic analyses into the STV space for establishing, augmenting, or rebutting relationships between key evidences and personnel involved in a story-mode
Funding

Please see our Scholarships page to find out about funding or studentship options available.

How to apply
Fostering sustainable development through pro-social immersive technologies.
Outline
Understanding and acting upon sustainability is essential for the development of our global world. In 2015 the United Nations released an agenda of 17 key goals to be achieved by 2030 in order to transform our world in a properly sustainable environment. These goals relate to key social, environmental, economic and cultural problems, covering issues such as: poverty; hunger; health; well-being; gender equality; education; availability and management of water supplies, food and energy; employment; economic growth; climate change; etc. This project addresses selected targets of the 2015 UN agenda through developing the following research lines: • Fostering the development of complex problem solving and systems thinking skills through the use of games and simulations • Enhancing healthcare, social care and quality of life for sufferers of high social impact diseases (e.g. dementia) and disorders (e.g. autism), through the use of adaptive assistive technologies and game-based engagement strategies • Enhancing environmental education and public awareness through the use of social games, simulations and augmented reality technologies • Fostering social awareness and engagement through the use of social media and interactive entertainment technologies • Fostering learning and education for sustainable development in formal and informal environments, through the use of social media and interactive entertainment technologies • Investigating sustainability behavioural patterns through leveraging big data, machine learning and context-aware computing.
Eligibility
The standard entry requirement for PhD study is a first or upper second-class honours degree, or the overseas equivalent, in a relevant subject. In certain circumstances, a lower second-class honours degree supplemented by a master’s degree, or appropriate relevant work experience, may be acceptable.
Funding

Please see our Scholarships page to find out about funding or studentship options available.

Deadline
Standard University deadlines Apply: http://www.hud.ac.uk/researchdegrees/howtoapply/
How to apply
In-Transit Analytics of data streams from Internet of Things (loT) devices
Outline
The Internet of Things (loT) is an exciting development of a collection of emerging technologies that bring together physical objects to exchange data with each other via wireless and physical network infrastructure. One example of particular interest is the potential of Wireless Sensor Networks (WSN), combined with computational power at the 'edge' of a network (otherwise known as 'Edge Computing' or 'Fog Computing'), to provide data that is analysed both at source and along the network path, to an eventual destination. Such data is produced in massive volume and at high velocity, which is generally beyond the limits of current technologies for storage and processing. This PhD project would investigate approaches to performing real-time analytics upon data from a range of devices (from simple sensors and embedded systems, right through to complex multi-cloud distributed repositories), to enable valuable insight to be delivered across networks with limited bandwidth. There will be a need to develop proof-of-concept prototypes for the purposes of benchmarking and evaluation. Applicants should be comfortable with high-level programming, as well as being able to specify and configure distributed file system software and analytics platforms. A background in networking (particularly ad-hoc/opportunistic wireless) will be useful, as will familiarity with data from Field Programmable Gate Array (FPGA) devices.
Eligibility
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.
Funding

Please see our Scholarships page to find out about funding or studentship options available.

Deadline
Standard University deadlines apply see - http://www.hud.ac.uk/researchdegrees/howtoapply/
How to apply
Innovative Learning Techniques for AI Planning.
Outline
The planning performance of state-of-the-art domain-independent planners can be improved by deriving and exploiting knowledge about the domain structure that is not explicitly given in the input formalisation. The type of knowledge to extract and the way for exploiting it, are very interesting topics for the Artificial Intelligence community. Three main knowledge extraction and exploitation approaches have been investigated: reformulation, configuration and combination. Reformulation techniques focus on changing the way the model is described; configuration approaches concentrate on adapting the planner to the specific problem, while combination methods improve overall planning performance by combining different planning systems. This project will investigate innovative techniques for the extraction and exploitation of knowledge in AI planners, in order to improve either the runtime –i.e., the time required for solving a problem– or the quality of generated plans. Specifically, the focus will be on the investigation of mixed reformulation-configuration techniques; some preliminary results (published at IJCAI 2015) confirm the area is promising and worthy investigating.
Eligibility
The standard entry requirement for PhD study is a first or upper second-class honours degree, or the overseas equivalent, in a relevant subject. In certain circumstances, a lower second-class honours degree supplemented by a master’s degree, or appropriate relevant work experience, may be acceptable.
Funding

Please see our Scholarships page to find out about funding or studentship options available.

Deadline
Standard University deadlines Apply: http://www.hud.ac.uk/researchdegrees/howtoapply/
How to apply
Innovative Learning Techniques for Improving the Performance of Al Planning Engines.
Outline
The development of domain-independent planners is leading to the use of this technology in a wide range of applications. This is despite the complexity issues inherited in plan generation, which are exacerbated by the separation of planner logic from domain knowledge. The planning performance of domain-independent engines can be improved by exploiting automatically derived knowledge about the domain or problem structure that is not explicitly given in the input formalisation. The type of knowledge to extract and the way for exploiting it are very interesting topics for the Artificial Intelligence community. At the state of the art, three main approaches have been investigated: reformulation, configuration and combination. Reformulation techniques focus on changing the way the model is described and provided as input; configuration approaches concentrate on adapting the planner to the specific problem, by changing its internal behaviour. Finally, combination methods improve overall planning performance by combining different planning systems and/or different learning approaches. This project will investigate innovative techniques for the extraction and exploitation of knowledge in Al planners, in order to improve either the runtime -i.e. the time required for solving a problem - or the quality of generated plans.
Eligibility
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.
Funding

Please see our Scholarships page to find out about funding or studentship options available.

Deadline
Standard University deadlines Apply: http://www.hud.ac.uk/researchdegrees/howtoapply/
How to apply
Knowledge extraction and embedding artificial intelligence in engineering processes.
Outline
Artificial intelligence is widely used under a ‘black-box’ philosophy within engineering processes aimed at automation and optimisation. For example, path planning is used to determine optimal cutting paths for CNC machine tools and neural networks are used to predicting thermal behavior of machine tools. Although the underlying technology will change for different applications, the requirement to capture, extract and utilise domain knowledge is consistent throughout. This will involve a domain expert with in-depth knowledge to determine what knowledge is required, and how it can be encoded in a suitable form for AI. This creates a ‘bottleneck’ in AI utilisation as the implementation can only ever be as good as the extracted knowledge. This project will investigate and develop tools to assist in knowledge extraction and analysis to minimise the requirement on expert knowledge.
Eligibility
The standard entry requirement for PhD study is a first or upper second-class honours degree, or the overseas equivalent, in a relevant subject. In certain circumstances, a lower second-class honours degree supplemented by a master’s degree, or appropriate relevant work experience, may be acceptable.
Funding

Please see our Scholarships page to find out about funding or studentship options available.

Deadline
Standard University deadlines Apply: http://www.hud.ac.uk/researchdegrees/howtoapply/
How to apply
Machine Learning of Domain Models for Long Term Autonomy and Explainable Al.
Outline
Current Autonomous Agents with cognitive capabilities based on an internal domain model can perform human like reasoning, and explain their reasoned decision on the basis of their domain model and their situational awareness. Unfortunately, state of the art systems have little or no learning / adaptation capabilities in order to maintain their understanding of the world, and hence maintain their reasoning and explanation capability over time. This project will be aimed at techniques for the knowledge acquisition and maintenance of the domain models, and use the capability of automated planning over a long term horizon as a method to measure progress.
Eligibility
The standard entry requirement for PhD study is a first or upper second-class honours degree, or the overseas equivalent, in a relevant subject. In certain circumstances, a lower second-class honours degree supplemented by a master’s degree, or appropriate relevant work experience, may be acceptable
Funding
Please see our Scholarships page to find out about funding or studentship options available.
Deadline
Standard University deadlines apply: http://www.hud.ac.uk/researchdegrees/howtoapply/
How to apply
Machine Learning-Augmented Vision System for Simulating Humanoid Robot Cognition
Outline
Complex scene understanding has been widely acknowledged as the ultimate aim of computer vision systems and an enabler for general purpose intelligent robotics. Accompanied by advancements in Artificial intelligence, biological vision and cognitive science, as well as engineering feasts in micro-processors , miniature sensors, and network and communication technologies, it is anticipated that this technology integration trend will bring economic and societal benefits in terms of new products, automation, smarter ways of working, better security monitoring and assistive technologies. The aim of this project is to investigate the utilisation of machine learning (ML) technology for processing and validating sensor signals to produce information that could be useful in high-level cognitive models. The project's results would be evaluated by application within a humanoid robot setting. The project will start from the creation of a general purpose, integrated and flexible machine vision system that can facilitate high-level decision making in complex circumstances. The anticipated challenges include augmenting additional signal sources, boosting critical signals in a noisy environment, and the creation of a feasible machine learning framework for the automatic compilation of symbolic knowledge.
Eligibility
The standard entry requirement for PhD study is a first or upper second-class honours degree, or the overseas equivalent, in a relevant subject. In certain circumstances, a lower second-class honours degree supplemented by a master’s degree, or appropriate relevant work experience, may be acceptable
Funding
Please see our Scholarships page to find out about funding or studentship options available.
Deadline
Standard University deadlines Apply: http://www.hud.ac.uk/researchdegrees/howtoapply/
How to apply
Mobile Apps-based Crime Scene Data Fusion and Augmentation
Outline
A crime scene can be where the crime was committed, or places where crime-related transportation, storage, and disposal acts had occurred. All locations where there is the potential for the recovery of evidence must be handled in the same careful manner. It is usually achieved by cordoning off wide areas around the crime scenes so that evidences can be carefully recorded in great detail. To reduce the interferences with the normal way/pace of public life, it is often a dilemma between "speeding up" the process and to "reduce" the risk of oversight. Modern computer and communication technologies have greatly facilitated crime scene data retrieval, management, and even the efficient manner of "on-line" analysis; for example, through harnessing the on-board GPS locating function, the camera (for image feature processing based operations), or the audio recorder (for acoustic based analysis). This project aims at exploring an innovative approach to develop, utilising, and integrating mobile application programs (or mobile apps) to offer increased productivity on information processing considering limitations of mobile device and processor specification and configurations.
Eligibility
The standard entry requirement for PhD study is a First or Upper Second class honours degree or the overseas equivalent in a relevant subject. In certain circumstances a Lower Second class honours degree supplemented by a Masters degree or appropriate relevant work experience may be acceptable.
Funding
Please see our Scholarships page to find out about funding or studentship options available.
Deadline
Standard University deadlines Apply: http://www.hud.ac.uk/researchdegrees/howtoapply
How to apply
Order invariant classification and outlier mitigation through sample modification, model selection and by using novel approximation estimators
Outline
Supervised learning requires training data to optimise parameters in a given model - such as an artificial neural network. Often the data will describe a range of classes or groups and the model will be required to learn from the data to enable new values to be predicted from unseen samples (prediction networks) or be identified as belonging to a particular class of features (classification networks). The generalisation capabilities of these models can depend on factors such as training data, network topology and training method. If some feature sets contain many more data samples than another's, bias can be introduced into the learning process because of the way in which the samples are presented to the network; this can cause difficulties in producing reliable models. Data reduction methods, to "even out" this effect, can lead to a loss of information and poor generalisation capabilities. Though it is possible to control the learning rate to some extent during training for some models, this is often arbitrarily done and is far from ideal. This project will aim to answer four key questions 1. Can the data be pre-processed/sorted effectively during each training epoch to allow order invariant classification to take place? 2. Can the topology of the network be pre-constructed and/or modified during training so as to reduce sample bias and increase classification accuracy across all features? 3. Can novel approximation estimators such as modified least squares methods and others, be used to asymptotically limit the effect of outlier bias on the training process? 4. To what extent would any of the above affect current solution methods - such as gradient descent methods, back propagation etc., and how will this affect known convergence rates?
Eligibility
The standard entry requirement for PhD study is a first or upper second-class honours degree, or the overseas equivalent, in a relevant subject. In certain circumstances, a lower second-class honours degree supplemented by a master’s degree, or appropriate relevant work experience, may be acceptable.
Funding
Please see our Scholarships page to find out about funding or studentship options available.
Deadline
Standard University deadlines Apply: http://www.hud.ac.uk/researchdegrees/howtoapply/
How to apply
Semantic and Knowledge Technologies for the Internet of Things.
Outline
The Internet of Things (IoT) is the vision of a network of physical objects (“things”) The Internet of Things (IoT) is the vision of a network of physical objects (“things”) equipped with sensors, software and networking capabilities which enable these objects to collect and exchange data. The PhD project would investigate approaches and develop novel methods for (a) enriching IoT data by linking them to ontologies and other data and information sources, and (b) providing reasoning services for processing IoT data at a high level of abstraction. Addressing (a) would be a major step towards achieving a Web of Things which would be siting on top of IoT functionalities (just like the WWW is residing on top of the Internet). Addressing (b) would enable the intelligent processing of huge amounts of IoT data, and requires to overcome major challenges in terms of the size and dynamicity of IoT data, among others. The project is suitable for a PhD student who has already acquired significant knowledge on semantic and knowledge technologies, e.g. in the areas of semantic web, linked data management, knowledge representation and reasoning, or logic programming. equipped with sensors, software and networking capabilities which enable these objects to collect and exchange data. The PhD project would investigate approaches and develop novel methods for (a) enriching IoT data by linking them to ontologies and other data and information sources, and (b) providing reasoning services for processing IoT data at a high level of abstraction. Addressing (a) would be a major step towards achieving a Web of Things which would be siting on top of IoT functionalities (just like the WWW is residing on top of the Internet). Addressing (b) would enable the intelligent processing of huge amounts of IoT data, and requires to overcome major challenges in terms of the size and dynamicity of IoT data, among others. The project is suitable for a PhD student who has already acquired significant knowledge on semantic and knowledge technologies, e.g. in the areas of semantic web, linked data management, knowledge representation and reasoning, or logic programming.
Eligibility
The standard entry requirement for PhD study is a first or upper second-class honours degree, or the overseas equivalent, in a relevant subject. In certain circumstances, a lower second-class honours degree supplemented by a master’s degree, or appropriate relevant work experience, may be acceptable.
Funding

Please see our Scholarships page to find out about funding or studentship options available.

Deadline
Standard University deadlines Apply: http://www.hud.ac.uk/researchdegrees/howtoapply/
How to apply
Using Higher Order Logic Provers for Answer Set Programming.
Outline
Recent advances in Higher Order Logic (HOL) provers suggests their use as problem solving engines also in other paradigms. One candidate is Answer Set Programming (ASP), since the semantics of ASP can be formalised using a second-order logic formula. The project could follow two streams of research: 1. Using the HOL prover for query answering in ASP. Current ASP solvers follow an architecture that first grounds the entire program, and then looks for answer sets. This approach is quite prohibitive for large amounts of data, in particular when doing query answering rather than model building. HOL provers are a potential way of circumventing the grounding bottleneck. 2. ASP allows for the definition of constructs that have a HOL 'flavour' – external atoms. There are several approaches for defining their semantics, one of them by means of HOL. This semantic characterisation could serve as an executable specification. For both streams, new tools would be constructed based on HOL and empirically evaluated. This project is in the area of logic-based Artificial Intelligence, and is appealing for both the traditional computational logic community and the AI and knowledge representation community. It is therefore likely that the project will generate REF-able output By nature, it fits very well in the research areas of the PARK group. The supervisor currently supervises two FWS.
Eligibility
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Funding

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Deadline
Standard University deadlines Apply: http://www.hud.ac.uk/researchdegrees/howtoapply/
How to apply

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