Partnerships Office

Current ICase PhD Student Vacancies


PhD Title: Identifying a holistic approach to management of ash die back disease

The studentship is with University of Salford and Rushmore Estate, University of York, Chester Zoo, DEFRA, Natural England, Henry Hoare & Co and The Salisbury Trust

Academic Supervisor: Dr Rachael Antwis

Academic Co-Supervisor Dr Stephen Parnell (University of Salford) and Dr Andrea Harper (University of York)

Industrial Supervisor: Andy Poore, Rushmore Estate and Dr Sue Walker, Chester Zoo

The studentship is fully funded and includes:

  • A fee waiver
  • A stipend of £15,943 p.a. for three and a half years
  • All bench fees and consumable costs
  • Funds specifically allocated for conference travel

Final date for applications: Friday 16th November 2018

Interviews will be held on: Monday 4th December 2018

The candidate must be in a position to register for a January 2019 start.


Invasive pathogens increasingly threaten forest trees, endangering associated biodiversity and reducing ecosystem benefits for humans (Forestry Commission, 2017). Strategies are urgently needed to reduce infection rates and increase the survival. Europe’s ash trees have been experiencing high mortality from the fungal pathogen Hymenoscophus fraxineus that causes ash dieback disease (ADB) and is now present across the UK. This project will take a holistic approach to identify environmental, host genetic and microbial factors to fully understand the factors that promote resistance to ADB. Based on this, the candidate will work closely with project partners to develop a management strategy for ADB that aims to protect our native ash trees.


Candidates should have at least a 2.1 at undergraduate level in a life sciences discipline, and ideally either a Master’s degree or a peer-reviewed publication.The student will receive first-class training in field and laboratory skills, including ecological surveying, next-generation sequencing and other molecular analyses. S/he will also receive experimental design and statistical analysis training including code-based bioinformatics. S/he will also interact with the various project partners through meetings, presentations and reports, resulting in the development of applied scientific research relevant to current conservation challenges and an improved awareness of how to work collaboratively and effectively with multiple groups. This project will involve field work in southern England. The candidate will require a full, clean driving license.

Funding Eligibility:

This studentship is only available to students with settled status in the UK, as classified by EPSRC eligibility.  Please visit:

Enquiries: Informal enquiries may be made to Dr Rachael Antwis via email to

Prospective students should apply by sending your Curriculum Vitae and supporting statement explaining your interest and suitability for the project (as a PDF file) to and by 5pm on Friday 16th November 2018.


Industrial Masters by Research Title: Automatic Dynamic EQ for Bass-Room Correction in Live Music

The studentship is with University of Salford and Music Tribe

Academic Supervisor: Dr Bruno Fazenda

Academic Co-Supervisor: Dr Jonathan Hargreaves

Industrial Supervisor: Dr Alessandro Palladini

The studentship is fully funded and includes:

  • A fee waiver
  • A stipend of £15,824
  • All bench fees and consumable costs
  • Funds specifically allocated for conference travel

Final date for applications: 10th November 2018

Interviews will be held on: by 25th November 2018

The candidate must be in a position to register by January 2019.

Description: The problems of room modes (resonances) is well known in the field of audio reproduction. These cause alterations in the frequency and time response of the sound field within rooms, affecting the correct and acceptable perception of sound within them.

In live sound applications, correction for modal problems is known to take a considerable time amount of a sound engineer’s time, calibrating and tuning of the sound system. An automatic system which can aid the engineer will expedite the process and lead to improved experience by both the engineer and the audience.

In a previous research collaboration between the university and Music Tribe, it has been acceptably established that a real-time dynamic solution is possible and affords tangible improvement in the process. This MSc allows the continuation of such collaboration with a focus on new research and methods to establish a better solution to the problem.   The aim of the project proposed is to design and implement an automated dynamic bass Equalizer guided by a new measure of bass quality index.

Candidates: Applicants will be expected to hold an upper second class honours degree or better in engineering, acoustics & audio, science, computing or maths. They should demonstrate excellent communication skills through a variety of modes of communications, with a diverse range of individuals. Analytical and programming skills are essential and some prior experience in machine learning techniques is desirable.  

As part of your application, please provide a CV, covering letter and research proposal. The proposal should include a brief literature review related to this project, with an outline of the studies that you would propose to answer the aims of the Masters. The proposal should be no more than 5 pages, single line spaced, 12-point font, Times New Roman.

For full details of student requirements and specification please visit:

Funding Eligibility:

This studentship is only available to students with settled status in the UK, as classified by EPSRC eligibility.  Please visit:

Enquiries: Informal enquiries may be made to Dr Bruno Fazenda by email:

Curriculum vitae, research proposal and cover letter (supporting statement) explaining their interest should be sent to


PhD Title: Maximising the benefit of Building Information Modelling through the seamless process of capturing As-Built Information into Asset Information Models for the efficient operation & maintenance of TfGM’s built assets

The studentship is supported by the University of Salford and Transport for Greater Manchester (TfGM)

Academic Supervisor: Professor Jason Underwood

Academic Co-Supervisor: Mr Andrew Fleming

Industrial Supervisor: Gemma Birchall

The studentship is fully funded and includes:

  • A fee waiver
  • A stipend of £15,824 p.a. for three and a half years
  • All bench fees and consumable costs
  • Funds specifically allocated for conference travel

Final date for applications: 9th November 2018

Interviews will be held on: Tuesday 27th November 2018

The candidate must be in a position to register by January 2019


An exciting and fully funded PhD studentship opportunity has arisen out of discussions between the University of Salford and TfGM.

For decades, UK Government and Industry reports have continually criticised the construction industry for its poor performance, low productivity, inefficient practices, adversarial behaviours, silo mindsets and for focussing too much on the short-term, rather than the long term and lowest price, rather than best value. These reports have not only provided a critique, but also a number of recommendations for improvements to the industry, in particular, to deliver better whole life value from built environment assets/estates from CAPEX through to operational management.

In May 2011, the UK Government launched its Construction Strategy, which included a formal commitment to drive change through the digital transformation of the UK construction industry. The primary driver of this was to address the issues of ensuring the UK derives full value from its centrally procured public sector built environment assets/estates. In terms of deriving improved whole life value from built environment assets/estates, this not only includes the capital delivery, but also deriving improved value post-construction by developing the capability to determine and capture the required operational digital data during the capital delivery phase that can then seamlessly flow through to enabling the efficient operation of built environment assets/estates.

TfGM are continuing to develop their Digital Construction capability to gain operational efficiencies through quality Asset Information Models (AIM), particularly during the capital delivery phase. However, it is perceived there is insufficient research in the industry to enable developing their capital delivery through to operation capability. Therefore, the aim of this project is to use current TfGM projects as case studies to establish their Information Requirements for the handover As-Built Information model and explore how this model can be extracted, during capital delivery, to seamlessly flow through the lifecycle to become an Asset Information Model (AIM) and utilised of their built environments assets. It is proposed to achieve this aim through the following set of objectives:

  1. Determine state-of-the-art and best practice of the process for the seamless flow of information through capital delivery through to operation.
  2. Establish the current (as-is) capital delivery process of existing TfGM case study projects in relation to ascertaining the enablers and barriers to the production and delivery of the As-built (handover) information, together with establishing the level of alignment with best practice.
  3. Determine the Asset Information Requirements for operation and mapping this against the delivered As-built Information Models in order to:

1. ascertain whether it is capable of fulfilling the requirements, and

2. identify any gaps in the information and the process of its delivery, i.e. gap analysis.

  1. To propose and develop a seamless process to facilitate the transition of the As-built (handover) Information Model to a fit-for-purpose Asset Information Model, that can be utilised to facilitate the efficient operation of their built environments assets, and which can be scaled up through both retrofitting on existing assets and future redevelopment projects.
  2. To develop a process for maintaining and updating the Asset Information Model for future use, including operations (Asset Management) and retrofitting/redevelopment.
  3. To explore emerging future benefits of the use of the Asset Information Model for TFGM and feedback through an iterative loop to review and refine the requirements (process and information).

The following outputs are proposed from the study:

  • A holistic critical review of the current capital delivery and operational management practice to enable
  • TfGM to determine a baseline from which to learn and build on.
  • A practical process solution for the definition, capture and delivery of useable, quality asset information throughout the entire asset lifecycle to realise pre-determined benefits to TfGM.
  • Established future benefits of utilising the Asset Information Model for TFGM.
  • Enhancing the awareness and engagement of TfGM in the process to facilitate organisational-wide buy-in and driving their digital transformation.
  • Coalition between academia, industry, professional institutions and industry bodies to deliver industrywide benefits and best practice.


The preferred candidate must possess a good understanding of digital construction, information management, and whole life value in the built environment with specific focus on BIM, Lean Construction principles, process management and collaboration. An understanding of facilities and operational asset management is also highly desirable, but not essential.

Candidates will hold a minimum of an upper 2nd class degree in a relevant subject area. Completion of a Master’s degree in a relevant subject area is desirable, but not essential.

Candidates are asked to provide a personal statement describing their background, skills, academic interests and their motivation for doing a PhD in no more than 2 sides of A4. This should include evidence of satisfying the above requirements along with being able to work independently to a high standard, collaborate with others, and excellent scientific writing skills.

For full details of student requirements and specification please visit:

Funding Eligibility:

This studentship is only available to students with settled status in the UK, as classified by EPSRC eligibility. Please visit:

Enquiries: Informal enquiries may be made to Professor Jason Underwood by email:

Curriculum vitae and supporting statement explaining their interest should be sent to


PhD Title: Adaptive control of functional electrical stimulation (FES)

This PhD studentship is with the University of Salford and Shandong BetR Medical Technology Co. Ltd.

Academic Supervisor: Prof David Howard

Academic Co-Supervisor: Prof Laurence Kenney

Industrial Supervisor: Dr Mingxu Sun

The studentship is fully funded and includes:

  • A fee waiver
  • A stipend of  £15,824 p.a. for three and a half years
  • All bench fees and consumable costs
  • Funds specifically allocated for conference travel

Final date for applications: 6th January 2019

Interviews will be held in late January 2019

The candidate must be in a position to register by 1st April 2019


Stroke affects approximately 150,000 people in the UK each year, leaving over 300,000 people living with moderate to severe disabilities. After stroke, many people cannot use their affected arm, and this has considerable impact on their quality of life. A major problem for stroke rehabilitation is the limited availability of physiotherapists. Therefore, home-based rehabilitation systems that do not require the presence of a therapist are needed to give the best chance of recovery through motor relearning.

Functional Electrical Stimulation (FES) of muscles is a low cost solution, which directly activates paralysed muscles through electrical stimulation via skin-surface electrodes. It has great potential as a stroke rehabilitation tool, and can even help patients with severe hand arm paralysis. In contrast to traditional physiotherapy, FES provides a means of directly tapping into the nervous system, actively producing muscle contraction and movement, exciting many of the associated neural pathways. If this is synchronised with the patient’s efforts to carry out meaningful tasks, it provides afferent inputs associated with the intention to create functional movement. This provides the most appropriate set of neural inputs to promote learning. Although recent studies have reported significant success, the problem with existing FES systems is that they require specialist skills to set up, particularly for upper limb rehabilitation, and therefore require clinical engineering involvement for each patient, negating the potential benefits mentioned above.

The challenge for the future is to enable home use without supervision by a therapist, which would require a system that could, to some extent, replace the therapist’s role of: a) monitoring the patient’s short-term progress; b) adapting the exercise regime accordingly; and c) providing the patient with real-time feedback on their performance. This is particularly challenging and the PhD project will focus on solving two connected problems:

Intelligent monitoring of patient task performance

The aim will be to use body-worn sensors during FES-supported therapy sessions to derive measures describing task performance, including movement deficiencies (poor coordination), speed of task execution, smoothness of movement, and movement variability between task repetitions (which has been shown to be a good measure of the quality of motor control). These measures will then be used for real-time biofeedback purposes, providing the patient with information on their task performance during the therapy session and, hence, to some extent replacing the therapist’s role of providing feedback.

To achieve this, powerful regression algorithms will be used to map the body-worn sensor signals onto the aforementioned variables of interest. To allow the regression algorithms to operate more successfully, be resilient to noise and variability, and adapt to different patients, fast signal pre-processing techniques will be employed prior to the regression stage.

Adaptive control of FES support so that patients are always being challenged

As the patient improves, the FES controller should adapt in real-time so that the patient must still strive to achieve the task goals. At present FES control parameters are adjusted manually, by trial and error, and it is unclear how this can be formalised so that it can be automated.

Therefore, machine leaning techniques will be applied that will use patient task performance information to make in-session patient-specific decisions on FES control parameter adjustment and, hence, to some extent replace the therapist’s role of adapting the exercise regime. Two possible approaches to solving this problem are:

  1. Using a rule-based system based on the results of knowledge elicitation from a group of FES specialists.
  2. Using machine-learning methods that can automatically learn from FES specialists.


Candidates should have a first or upper second-class honours degree in an area relevant to the proposed research. This includes engineering, physics, mathematics or computer science. Candidates with other closely related first degrees should contact Prof Howard to discuss their eligibility.

For full details of student requirements and specification please visit:

Informal enquiries may be made to Prof David Howard by email:

A curriculum vitae and supporting statement, explaining your motivation and interests, should be sent to


PhD Title: Blast noise prediction and management

The studentship is with University of Salford and Spadeadam Testing & Research Centre (STaR), part of the DNV GL Group

Academic Supervisor: Professor David Waddington

Academic Co-Supervisor: Dr Sabine von Hunerbein

Industrial Supervisor: Paul Cronin

The studentship is fully funded and includes:

  • A fee waiver
  • An enhanced stipend of £16444 p.a. for three and a half years, plus paid accommodation at Spadeadam for years 2 and 3
  • All bench fees and consumable costs
  • Funds specifically allocated for conference travel

Final date for applications: 29 October 2018  

Interviews will be held on: 13 November 2018

The candidate must register by 14 January 2019


STaR and the University of Salford are looking for a PhD candidate to work with them on developing methods for explosive noise impact prediction and management. The successful candidate will be expected to undertake experimental trials, model long-range noise propagation prediction, and develop procedures to manage adverse human impact in the near and far field.


DNV GL is a global quality assurance and risk management company. Driven by their purpose of safeguarding life, property and the environment, they enable their customers to advance the safety and sustainability of their business. DNV GL provide classification, technical assurance, software and independent expert advisory services to the maritime, oil & gas, power and renewables industries. Part of the DNV GL Group, the Spadeadam Testing and Research Centre (STaR) conducts a wide variety of fire, explosion and blast testing, ranging from fundamental research into the hazards of the oil, gas and chemical industries to product testing of protective and mitigation systems.

DNV GL strategic aim is to make its Spadeadam Testing & Research Centre (STaR) a world leading test facility serving industries including, oil and gas, security, chemical and explosives.  STaR intends to grow its turnover from £7m at present to £20m over the next 10 years. To achieve this, it is necessary to ensure that the existing work is not put at risk from adverse reactions to noise nuisance in the surrounding areas.  Also improved far field noise prediction should allow:

  • Improved scheduling of trials, hopefully reducing costs from potential delays.
  • Larger explosion trials to be conducted without generating significant noise disruption.
  • Extending the trials season increasing the number of available trial slots

Currently the whole industry relies on an expensive and inaccurate commercial system for noise predictions. Having a test facility in the North of England puts STaR within one of the most diverse and unpredictable weather systems in the world. This requirement to predict blast noise propagation through complex meteorology means that the new and more accurate system to make predictions for the STaR site will need to be the first of its kind in the world.

STaR is likewise interested in researching new ways to reduce risk to hearing from short duration, high-level explosive noise to allow them to best protect their employees, visitors and clients. Optimising the management of explosive noise exposure will allow STaR to confidently maximise the number of test opportunities while minimising impulsive noise risks to hearing.


We are looking for high quality candidates with an expertise in the following areas;

  • Cognate acoustics, mathematical, physics, engineering, meteorology or computer sciences background
  • Good computer programming skills
  • Experience of experimental measurement, preferable environmental noise
  • Highly numerate with experience of data analysis
  • Ability to work independently and within teams
  • Strong written and verbal communication skills

For full details of student requirements and specification please visit:

Funding Eligibility:

To be eligible for the full maintenance grant, students must be a UK/EU National or be able to demonstrate a relevant connection with the UK, usually through being ordinarily resident for a period of 3 years immediately prior to the date of application for an award. Member states of NATO will also be considered

Enquiries: Informal enquiries may be made to Professor David Waddington by email: