PhD Opportunities

The School welcomes enquiries relating to hardware of telecommunications, computer networks, games technology, electronic engineering and software engineering. Areas of research in which staff are currently active include gaming, e-business, home automation, learning technologies, intelligent systems, security and forensics, robotics and cloud computing.

PhD opportunities - computing

Areas of interest

  • Machine Learning
  • Evolutionary Computation
  • Multi-Agent Systems
  • Knowledge Engineering

We find it most effective to work with inquirers to focus their research ideas before a formal application is made.  You can contact either individual staff or the School's Director of Research Degrees, Dr Chris Creed.

Cyber Physical Systems

Cyber Physical Systems

Developing innovative solutions across multiple disciplines for an increasingly digital society.
Find out more >>
DMT research tile

Digital Media Technology

Specialising in the development of methods for creating, processing, analysing, evaluating and distributing digital media.
Find out more >>
Data analytics and AI

Data Analytics and AI

Developing advanced machine learning and optimisation methods, applying multi-agent systems to smart cities and digital health.
Find out more >>

How to apply for a PhD

Formal applications should be made using the University's online application form, which can be found under the 'How to Apply' tab of the Computing PhD course page.

The form should be accompanied by an initial research proposal of 1,000 - 2,500 words (fully referenced) explaining your ideas about topic and how it might be studied - this allows us to match your ideas with staff experience and interests, and (if you wish) accompanied by a curriculum vitae.

To discuss the application process or request an application form please contact the CEBE Doctoral Research College (DRC.CEBE@bcu.ac.uk)

Computing Studentships

PhD Classic Doctoral Training Grant Funding Information

This funding model includes a 36 month fully funded PhD Studentship, in-line with the Research Council values, which comprises a tax-free stipend paid monthly (£16,062 for 2022-2023) per year and a Full Time Home Fee Scholarship (£4,596 for 2022-2023) for up to 3 years, subject to you making satisfactory progression within your PhD research.

International students will be required to pay the difference between the International Fee Rate and Home Fee Rate. All applicants will receive the same stipend irrespective of fee status.

Closing Application Dates:

Home Applicants (including Pre-Settled and EU Settled)
23:59 on Tuesday 31st May 2022 for a September 2022 start.
23:59 on Monday 31st October 2022 for a February 2023 start.

International Applicants
23:59 on Saturday 1st October 2022 for a February 2023 start.

Please include your start preference when applying.

How to Apply 

To apply, please complete the project proposal form,ensuring that you quote the project reference, and then complete the  online applicationwhere you will be required to upload your proposal in place of a personal statement as a pdf document. 

Formal applications should be made on the University's online application form, which can be found under the 'How to Apply' tab of the course page.

You will also be required to upload two references, at least one being an academic reference, and your qualification/s of entry (Bachelor/Masters certificate/s and transcript/s). 

To discuss the application process please contact DRC.CEBE@bcu.ac.uk.


Data-Driven process design for Real-Time Service Provisions in Urban Computing Environment.

Reference: UCE - VJ

Supervision team

Dr. Vahid Javidroozi (DoS); Dr. Adel Aneiba; Dr. Gerald Feldman

Background

Liveability in fast-growing cities depends on the ability to address urbanisation issues such as traffic congestion, pollution, health, infrastructure, and waste management. However, rapid urbanisation has led to a deficiency in service provisions, as transforming these services is time-consuming. To address these issues and for sustainable living in these fast-growing urban environments, changing the method of performing urban activities and functions is necessary, to provide agile and efficient services to the citizens in real-time. Urban computing technologies ameliorate the change from traditional services to data-driven services. Thus, urban computing as a technology is utilised to address the complexity of providing adequate services to citizens through facilitating cross-sectoral collaboration and enabling the integration of various city sectors/systems.

Motivation

The rate of change and fluctuating citizens’ demands in all areas of city management, such as living, economy, people, governance, mobility and environment is increasing, and the technologies of generating data that can be used to improve the service provisions are being enhanced. As a result, we are facing a big amount of data, which can also be converted to information and knowledge in various contexts. However, earlier research findings suggest that the data is not being effectively applied to improve the quality of current city services. This requires flexible, efficient, and integrated processes across various city sectors that effectively respond to the changing environment. As a result, this would allow for agile and efficient services that are supported by seamless communication amongst city components, sectors, and systems and availability of real-time information.

Proposed research

Urban computing uses ubiquitous computing technologies to gain a better understanding of how to improve our cities (Marciniak and Owoc, 2013). Urban Computing consists of four layers sensing, data management, analytics and service provision. While the sensing and data management layers are well established, it can be argued that to date there is a limited effort in utilising the data analytics layer to inform the design of processes to influence the service provision. It is propositioned that continuous analysis of data in real-time, allows immediate action to be taken when attempting to improve service provisions.

Several techniques can be used to bridge this gap by utilising the structured and unstructured data to design processes, referred to as Data-driven Process Design. Thus, it facilitates identifying process changes in real-time to improve performance and how we can offer service provisions. However, the lack of process layer in urban computing frameworks averts the efforts of sensing, analysing, and managing data to be practically applied for improving service provisions in urban areas. In addition, data-driven approaches can identify complex and non-linear patterns in data that can be utilised to design processes and process modelling, data management, and process mining. Hence, this research will explore some of these data-driven techniques, develop a model in order to offer a systematic approach for designing processes to support real-time service provisions.

Potential impact

Based on the principles outlined earlier, the outcome of this research will have the following potential implications through designing city services using real-time data:

  • Utilising valuable real-time data generated from IoT devices, sensor technologies, citizen inputs, etc. for improving service provisions in cities, especially emergency services, such as ambulance and fire services;
  • Providing real-time service provisions based on immediate citizens needs;
  • Improving the communication across city stakeholders as a whole and bringing them together as part of the decision-making process;
  • Improving the decision-making processes and proposing an automated application of decisions in service provisions;
  • Suggesting significant progress towards urban computing objectives, such as energy efficiency, waste management, economic growth, reducing carbon emission, Net Zero strategies, and so on;
  • Managing the resources for planning, designing, funding, and operating timely services.

To discuss UCE-VJ, please contact Dr Vahid Javidroozi

Email:
Vahid.javidroozi@bcu.ac.uk
Website:
https://www.bcu.ac.uk/computing/about-us/our-staff/vahid-javidroozi


Society-at-Large Inference (SALI) 

Project reference: SALI-PhD 

Contact:Iain.Rice@bcu.ac.uk

Background 

This PhD is sponsored by COVATIC - an innovative software development and Artificial Intelligence firm interested in ethically characterising individual behaviours through smartphone usage.

The connected digital world owes a debt to mobile communication through smartphones. The widespread use of social media has correlated with a surge in streaming services. Consumers of digital media in these forms who would traditionally have been disjoint, now share commonalities across traditional boundaries. Historically, media outlets and broadcasters controlled these communications and set trends and themes nationwide. However, research has shown that trends have transcended beyond the media and new issues have arisen. Individuals acting on these networks can be treated as nodes on a graph, where the linkages between nodes form a ‘Pattern of Life’.

The challenge is to identify latent structures in the connected network. Latent variable modelling is typically applied to static high-dimensional structured or dynamic low-dimensional domains. The connections are naturally stochastic, requiring the bridging of Graph Theory and Probabilistic Graphical Modelling to characterise themes forming paths. The future of themes, trends and network linkages is subject to inherent uncertainty. The further stages of the project will extend the algorithms into a dynamic framework to manage temporal evolutions. This framework will facilitate queries and inference allowing users to identify emerging trends and adaptively react, leading to structured, targeted marketing and broadcasting content. 

Essential Experience

  • A strong background in Artificial Intelligence/Machine Learning methods (e.g., Deep Learning, Reinforcement Learning, Evolutionary Computing, and other supervised and unsupervised learning methods). 
  • An MSc or bachelor's in Mathematics/Computer Science/AI/Data Science with a record of innovative projects (industrial projects, publications and/or open-source development); Or a bachelors in a STEM discipline and over 5 years of industrial experience in Data Science and Artificial Intelligence. 
  • Experience in programming in Python and exposure to libraries such as Pandas, Numpy etc. (Other suitable scientific programming languages would be acceptable - R/Julia/C/C++ etc.) 
  • A team player with a demonstrable attitude to enjoy scientific challenges.

Desirable Experience 

  • Publications in high impact factor journals and A/A* (core-ranking) rated conferences. 
  • Industrial experience in Data Science and Artificial Intelligence. 
  • Experience in mobile development (Android or iOS) - further training will be available. 
  • And experience with MongoDB (NoSQL databases) - further training will be available. 

Topics of Interest 

Machine Learning in Mobile App Environment, Edge Computing, Graph Theory, Probabilistic Graphical Modelling, ML Ops and Federated Learning. 


Permissions Agnostic Behaviour Tracking for Mobile Applications (PAB) 

Project reference: PAB-PhD 

Contact:atif.azad@bcu.ac.uk

Background  

This PhD is sponsored by COVATIC - an innovative software development and Artificial Intelligence firm interested in ethically characterising individual behaviours through smartphone usage.

Mobile devices are ubiquitous in the modern era: approximately 90% of UK adults own a smartphone. Therefore, mobile advertising has grown rapidly over the last decade, now accounting for more than half of all digital ad spend in the UK. One driver for this growth has been the user tracking properties of connected smart devices, which facilitates the behavioural targeting of users . Such tracking, whilst effective, has frequently concerns. These concerns have begun to manifest in regulations such as the General Data Protection Regulations (GDPR)  and the California Consumer Privacy Act (CCPA), which restrict the tracking activities that advertising networks conduct without the explicit permission of the user. This has also led mobile operating systems - most notably, Android and iOS - to tighten the circumstances under which mobile app developers can request certain app permissions, particularly permissions relating to user location and activity.

Traditionally, these behavioural tracking networks have collected data from devices (and across devices), associated this data with a tracking ID, and performed large scale data mining off-device to infer a user's behaviour, lifestyle, preferences etc. While these insights are used to match users with advertising campaigns, this off-device data mining produces large caches of personally identifiable data that can be processed, resold and exploited without the original user's knowledge; these caches are also a prime target for cyber-criminals. It is likely that such activities will continue to be restricted, regulated and/or prohibited in the future, which is leading advertising businesses to look for ways to continue to perform ad-targeting, but while emphasising on protecting the privacy of the end-user.

Challenge

Together with COVATIC, the PhD will use Machine Learning to segment the customer base and/or identify user's behavioural patterns while using (privacy agnostic) information that is unlikely to go behind privacy walls erected and further elevated overtime by the mobile operating systems. For example, a challenge is to infer geographical whereabouts without using the user's location information.  

Essential Experience

  • A strong background in Artificial Intelligence/Machine Learning, (e.g., Deep Learning, Reinforcement Learning, Evolutionary Computing, and other supervised and unsupervised learning methods).
  • MSc/bachelor's in computer science/AI/Data Science and industrial/open-source projects or publications; Or a bachelors in a STEM discipline but over 5 years of industrial experience in Data Science and AI. 
  • Experience in scientific programming: Python with Pandas, Numpy etc (or suitable experience in R/Julia/C/C++ etc)
  • A team player (showcased with STAR technique). 

Desirable Experience

  • Publications in high impact factor journals and A/A* (core-ranking) rated conferences; 
  • Substantial industrial experience in Data Science and Artificial Intelligence. 
  • Experience in mobile development (Android or iOS) and MongoDB - further training will be available.

Designing Haptic Navigation Systems for Sensory Substitution

Reference: CEBE-AT-01

Project Lead: Dr Arthur Theil

Project outline

Birmingham City University is inviting applications for the following PhD project supervised by Dr Arthur Theil in the Department of Digital Media Technology.

This is an exciting opportunity to conduct research into the next generation of tangible, haptic display technology for assistive purposes. The PhD student will design, prototype, and evaluate novel haptic systems for people with sensory impairments. These are sensor-based interfaces that translate visuo-auditory information into vibrotactile displays to support sensory substitution.

Independent mobility is a challenge faced by an estimated number of 15 million individuals living with deafblindness. In the UK alone, it is estimated that there are nearly 400,000 people living with a combination of sight and hearing impairments.

People with deafblindness can navigate their environment if they are familiar with the layout of the room, however, in an unfamiliar environment, an interpreter-guide is often needed.

Sensor-based technologies, if they are linked to haptic means of conveying information (e.g., vibration, pressure, touch), have the potential to make a significant difference, by providing sensory substitution and opening up identification of bystanders, unknown environments, obstacles, and things beyond the limited arm's reach often available to people with deafblindness.

The primary goal of the PhD project is to investigate the optimal use of haptic communication systems to support the development of assistive navigation technologies for people with deafblindness. The research will explore the design of wearable assistive systems and intuitive on-body haptic signals to communicate different environmental information, including but not limited to object, obstacle, and bystander detection as well as proximity and directional feedback in both indoors and outdoors environments.

The PhD work is expected to:

  1. Explore novel interaction methods for sensory substitution (visuo-auditory information to haptics) informed by user-centred design approaches.
  2. Design wearable sensing technologies that provide haptic navigation support to people with sensory impairments.
  3. Evaluate new sensing technologies and conduct user studies with people with deafblindness and professionals working with this population.
  4. Communicate research findings in scientific publications (journals and peer-reviewed conferences) in the fields of Human-Computer Interaction and Assistive Technologies.

The PhD student will be hosted in the DMT Lab (HCI research group) that specialises in innovative research around design, virtual/augmented reality, accessibility, assistive technology and sensor-driven interaction.

Person specification

We are seeking an outstanding candidate to undertake interdisciplinary PhD research in Human-Computer Interaction, Haptics and Assistive Technologies.

The candidate should have a background in Human-Computer Interaction, Computer Science, Wearable Computing, Engineering or similar, with enthusiasm for applied research in Assistive Technologies and Haptics.

The PhD candidate will need to demonstrate experience with programming (machine learning and computer vision are desirable skills) and physical prototyping tools (Arduino, Raspberry Pi). A strong interest in conducting user studies with people with sensory impairments is also desirable. Excellent writing and oral communication skills are essential.

To discuss Project CEBE-AT-01, please contact Dr Arthur Theil

Email:
Arthur.Theil@bcu.ac.uk


Natural Language Processing for automatic compliance checking in Architecture Engineering Construction Operation 

Project: NLP4AECO:
Reference: CEBE-EV-02
Project Lead: Dr Edlira Vakaj

Project outline

The compliance checking process occurs constantly throughout all phases of a project lifecycle in the AECO (Architecture, Engineering, Construction, and Operation) domain to ensure buildings are fit for purpose energy efficient, and constructed in accordance with the design specifications, functional and, safe to use, and sustainable to the environment throughout its service life. The process for compliance checking is laborious and demands the interpretation of regulations and guidelines. Automating the compliance processes will transform the building design, disrupting the unavoidable tedious checking and approval step and enabling designer to work with generative design using AI more ineffectively and effectively.  

The application of Natural Language Processing using a traditional approach (largely applicable for general-domain text processing) for this domain is challenging as building-code sentences typically have deeply nested syntactic and semantic structures, including recursive clauses, conjunctive and alternative obligations, and multiple exceptions. Recent efforts in NLP development have shown that semantic deep neural networks are capable of learning the complex syntactics and semantics of the natural language and thus, gives the potential for automated compliance checking. In this study, Named Entity Recognition (NER) tasks will be employed to identify a set of concepts and relations with reference to recently developed semantic models, e.g. BOT, DiCon ontology, widely adopted by W3C Linked Building Data Community Group leading the development of semantic webs for building models. 

The overall aim and objective is to design and implement a consolidated for generating computer processable rules from compliance regulations of AECO that integrates deep learning, transfer learning strategies, and both target- and general domain data to extract semantic and syntactic information fully automatically.  

Person specification

Qualifications Required  

  • A BSc in Computer Science or similar.  
  • MSc in Machine Learning, Data Science, or Artificial Intelligence.  
  • 2-3 years of experience in full-stack machine learning engineering will be a great advantage. 
  • Required Level: Excellent  
  • Analytical skills and the ability to work independently as well as in a team.  
  • Written and oral communication skills in English. 
  • Programming skills in Python.
  • Required Level: Very Good  
  • A strong understanding of artificial Intelligence/ Machine Learning methods.  
  • Natural Language Processing libraries in Python (Spacy, SciKit-Learn, NLTK ).  
  • Required Level: Good  
  • Ontologies 
  • Semantic Web Technologies 

Other Qualifications (Desirable)  

  •   Familiar with the Build Environment domain applications. 
  •   Familiar with Linked Building Data. 
  •   Publications in high impact factor journals. 

To discuss Project CEBE-EV-02, please contact Dr Edlira Vakaj

Email:
Edlira.vakaj@bcu.ac.uk


Adaptive security for low energy encryption in IoT 

Project outline

Reference: CEBE-ST-09

Project Lead: Dr Sandy Taramonli

With the rapid evolution of IoT in recent years, new cyber security risks and challenges are arising, such as how to handle huge amounts of data, while addressing security threats with the use of low or minimum power and memory resources; especially for battery powered devices, or low resource devices, it is important that the adequate level of security is provided, at the lowest cost of energy, taking into consideration the device state and resources available. 

This project will be focusing on the limitations of energy implication in IoT security. It will investigate the optimum security mode selection in terms of the energy consumption taking into consideration the device state, aiming to suggest a model for energy-conscious adaptive security in IoT communications. Stochastic and statistical methods will be implemented, in order to evaluate the performance of the security modes and propose an adaptive system that can be used as a flexible decision-making tool for selecting the most efficient security mode at the lowest cost of energy. A decision-making framework will be developed that evaluates the overall impact of each device state parameter, namely data size, memory space, computation power, and battery power on energy consumption and therefore identify the optimum security mode. It is aimed that for resource-constrained devices, which have less computation power, limited battery life, a small amount of memory, and low bandwidth, an efficient security solution will be developed, which is required so that it will not exhaust the resources of IoT devices.

The aims of this project concern the maximization of encryption strength and its performance in terms of energy consumption management, taking into consideration several inter-related factors based on the device state. It is intended to develop a framework for the decision making regarding the most efficient security mode selection. To this end, the following objectives have been set out:

  1. To identify the limitations of existing security approaches in IoT
  2. To deploy stochastic and statistical methods, including reliability, concentration inequalities and regression analysis to model the device’s state parameters and energy consumption
  3. To build a stochastic model that represents the device states which can be utilised to estimate the energy consumption
  4. To examine the impact of the device state parameters on energy consumption
  5. To investigate how a security framework can utilise this information to maximize encryption performance
  6. To develop a decision-making tool for energy consumption management in IoT encryption
  7. To evaluate the framework using an IoT testbed and compare against existing approaches based on the existing metrics and thresholds 

Person specification

  • A first-class or upper-second-class honours degree/MSc in Cyber Security, Mathematics, Computer Science or related fields
  • A research interest in Cyber Security and IoT
  • Experience in modern programming languages
  • Background in Statistics and Probabilities
  • Good analytical skills, knowledge of foundations of computer science
  • Ability to think independently
  • Ability to develop understanding of complex problems and apply in-depth knowledge to address them
  • Excellent organisational, communication and problem-solving skills 

To discuss Project CEBE-ST-09, please contact Dr Sandy Taramonli

Email:
Sandy.taramonli@bcu.ac.uk
Website:https://www.bcu.ac.uk/computing/about-us/our-staff/sandy-taramonli


A Systemic Digital Transformation towards developing Smart NetZero Cities

Reference: CEBE-VJ-10

Project Lead: Dr Vahid Javidroozi

Project outline

Since Net Zero strategy is about decarbonising “all” sectors of the economy, establishing a cross-sectoral integration approach and inter-sectoral collaboration is necessary to achieve the Net Zero goals, which should be common amongst the city sectors. According to a “systems approach” of the UK’s government Net Zero Strategy (sections 3 to 8 of the journey to Net Zero), the journey towards Net Zero cities necessitates consideration of a ‘systems thinking’ approach to enable a digital transformation across various city sectors/systems. Likewise, city systems integration is a significant requirement of Smart City Development (SCD), which is currently being undertaken by city authorities to mitigate urbanisation challenges, through providing real-time integrated and efficient data-driven services across city systems/sectors.

Hence, the meaning of ‘Smart NetZero’ in the context of future cities does not only include the necessity of NetZero agenda but also comprise the concept of Smart City Development (SCD) as a facilitator for moving towards Net Zero cities. In addition, digital transformation of cities towards SCD from multiple dimensions, including process, data, people, and technology has become a common understanding in the academia, especially within socio-technical research groups. Therefore, this PhD project will focus on the systems approach of the Net Zero strategy and will combine the abovementioned concepts, exploring the requirements of developing NetZero cities from various aspects through considering the 2050 NetZero strategy and Smart City Maturity Models (SCMMs) / frameworks. 

This research aims at developing and validating a roadmap for systemic digital transformation of cities to move towards Smart NetZero Cities. The following preliminary objectives will be addressed as part of this research:

  • To better understand the requirements of NetZero agenda in the context of smart cities;
  • To identify various dimensions of smart city development as well as NetZero city development, and develop a conceptual framework for Smart NetZero Cities;
  • To deliberate the requirements of Smart NetZero cities within every aspect of Smart NetZero Cities; 
  • To identify the priorities of developing Smart NetZero cities through a quantitative research;
  • To develop a digital transformation roadmap for Smart NetZero Cities by utilising the learnings from existing digital transformation roadmaps and the findings of this research;
  • To validate the roadmap through a focus group strategy.     

Person specification

The applicant must have academic qualifications related to one (or more) of the following (or similar) fields: information systems, digital transformation, smart cities, sustainability, Green/Net Zero agenda. Having an Interdisciplinary qualification is an advantage. The applicant should demonstrate that they are able to;

  • Undertake original academic research; 
  • Conceptualise, design, connect, implement multiple disciplines for the generation of new knowledge and/or applications, and to adjust the design in the light of unforeseen problems;
  • Apply techniques for research and advanced academic enquiry to address complex issues; 
  • Understand holistic approaches, whole systems, systems thinking, digital connectivity, interconnection, systems integration.

To discuss Project CEBE-VJ-10, please contact Dr Vahid Javidroozi

Email: 
Vahid.javidroozi@bcu.ac.uk
Website: 
https://www.bcu.ac.uk/computing/about-us/our-staff/vahid-javidroozi

Digital Media and Technology studentships

PhD Classic Doctoral Training Grant Funding Information 

This funding model includes a 36 month fully funded PhD Studentship, in-line with the Research Council values, which comprises a tax-free stipend paid monthly (£16,062 for 2022-2023) per year and a Full Time Home Fee Scholarship (£4,596 for 2022-2023) for up to 3 years, subject to you making satisfactory progression within your PhD research.

International students will be required to pay the difference between the International Fee Rate and Home Fee Rate. All applicants will receive the same stipend irrespective of fee status.

Closing Application Dates:

Home Applicants (including Pre-Settled and EU Settled)
23:59 on Tuesday 31st May 2022 for a September 2022 start.

23:59 on Monday 31st October 2022 for a February 2023 start.

International Applicants
23:59 on Saturday 1st October 2022 for a February 2023 start.

Please include your start preference when applying.

How to Apply 

To apply, please complete the project proposal form,ensuring that you quote the project reference, and then complete the online applicationwhere you will be required to upload your proposal in place of a personal statement as a pdf document. 

Formal applications should be made on the University's online application form, which can be found under the 'How to Apply' tab of the course page.

You will also be required to upload two references, at least one being an academic reference, and your qualification/s of entry (Bachelor/Masters certificate/s and transcript/s). 

Project Title: Novel Augmented Reality for Radiotherapy Training, Preparation and Delivery

Reference: CEBE-MAK-04

Project Lead: Dr Maadh Al Kalbani.

Project Outline

This project key aim is focused on the design, development, and evaluation, of a novel wearable AR system for therapeutic radiographers and trainees in the live clinical setting. The system will provide interactive augmentations of complex radiotherapy treatment data in real time. This will visually represent information from patient treatment plans and include dose parameters and the 3D patient anatomical models.

Radiotherapy is a common treatment for many forms of cancer. The treatment process of Radiotherapy uses very high energy X-rays doses and relies on the interpretation of multiple sources of patient data to define a comprehensive treatment plan. This project proposes an AR solution to present the correct 3D patient centric data at the correct point in the planning and setup workflow to clinical radiographers. It will involve interfacing with complex multidimensional patient data and clinical systems. The use of AR has proven to be positive and in alignment with standard training practices with promising results in wider areas of medical training. Therefore, this PhD offers an exciting opportunity to research, design, develop and evaluate a novel system for improving both the training and preparation of radiotherapy treatment.

Main objectives are:

  • Review the current state of the art for AR within a live clinical setting
  • Evaluate the potential designs for applying AR to support setup, treatment planning and radiotherapy delivery
  • Develop methods and modes for providing guidance for clinical radiographers and trainees in a live clinical patient setup
  • Evaluate the proposed development, considering state of the art evaluation methods relating to accuracy, treatment time, robustness, cognitive load, and value
  • Discuss potential for transforming the radiotherapy treatment process and delivering global impact in transformative clinical care
This PhD research will support a seamless interface with interactive 3D patient data and will be used by trainees and practitioners to understand how the data leads the treatment and how the treatment interplays with the patient. This will provide a richer experience for learners and practitioners and lead to improved treatment delivery.

Person Specification

The applicant should have a good bachelor’s degree (2.1 or above) in a computer science related discipline. A strong programming background in relevant languages (C#, C++, Python) and software tools / libraries / devices (Unity, Unreal Engine, Vuforia, OpenGL, HoloLens, Quest, HTC Vive or similar) is essential. A master’s (MSc) degree or equivalent professional experience in applied Computer Science, Computer Graphics, Visualisation, Virtual Augmented and Mixed Reality, Digital Media Technology, IT or other related computer science discipline is highly desirable. The candidate is also expected to visit NHS Foundation Trust partners as part of the project and therefore excellent interpersonal skills are essential.

To discuss Project CEBE-MAK-04, please contact Dr Maadh Al Kalbani:

Email: maadh.alkalbani@bcu.ac.uk

Website: https://www.bcu.ac.uk/computing/research/digital-media-technology/research-subgroups/mixed-reality-and-human-computer-interaction