PhD Project opportunities

Welcome to our "project matching" service for the current Industrial Doctorates call for funding. The following list shows a range of potential PhD / EngD projects suggested by academics from Scottish universities.


The following project opportunities are open for Scottish businesses or public sector organisations interested in collaborating with Scottish Universities and benefit from an applied research project with a doctoral student. Funding is available through our current call for funding. If any of the following projects are of interest to you or your organisation, please get in touch.

If you are a prospective doctoral (PhD / EngD) student interested in applying for one of our current vacancies, please visit our industrial doctorates page.



Energy Forecasting and Applications

University of Strathlcyde, Jethro Browell

Core themes: Probabilistic forecasting methodology and use in decision-making.

Innovation in: post-processing numerical weather forecasts using contemporary statistical methods, decision-making under uncertainty, modelling the interaction between weather and energy systems.

Scotland has an enormous wind and marine energy resource which must be forecast on an operational scale. To maximise the utilisation of this resourse operational forecasts must be accurate and provide relevant information to decision-makers, who in turn must be equipped with the tools required to make optimal decisions in the presence of multiple uncertainties.

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Information and User Based Adaptations for Data Intensive Interfaces

Robert Gordon University, Dr. Michael Crabb

The intended approach for this work draws inspiration from the accessible computing field where it is common knowledge that a one-size-fits-all approach does not work within a design. We anticipate that the same is true within data-intensive environments and wish to explore how interfaces can be tailored based on an individual’s abilities and on the data that is currently being examined.

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Reducing the uncertainty in miaging when using sensor array data

University of Strathclyde, Tony Mulholland

An array of sensors can be used to position and image an object in its field of view. However, one source of uncertainty that arises in this exercise is due to wave refraction. Our approach will be to use sensor data to reconstruct this heterogeneous spatial map of the local wave velocity. This knowledge will then be used to discount the refractive effects and thereby reduce the uncertainty of this imaging problem.

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Balanced Orientation: Aligning employees' resources and capabilities to the needs of Internal & External Stakeholders to improve market performance

University of Strathclyde, Prof. Spiros Gounaris

To improve market share and profitability, service organisations rely on offering superior customer experience and value. To achieve this, they have to develop a customer oriented culture and values to guide the delivery of their service. However, it is well established that in addition to the customer market, companies need to be competitive in the employee market, satisfying employees’ needs; particularly in services.

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Design a fully automated and rapid analyser for pathogen detection in water based on ultrasound and lab-on-ship technologies.

Edinburgh Napier University, Dr. Abdelfateh Kerrouche

Drinking water is the subject of strict regulations aiming at controlling the existence and virulence of pathogens such as Cryptosporidium responsible of acute diarrhoeas and sometimes deaths in infants and immune-deficient adults. This project aims to develop ultrasounds to be applied in the first stage and lab-on-ship technology for the second stage of the detection protocol.

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A Smartphone-based Universal Vision Sensor Combining a Software Retina and Deep Learning

University of Glasgow, Dr. Jan Siebert

In this project we propose to develop a low-cost and highly integrated camera sensor for egocentric and robotic vision applications. Our goal is to address a key issue in designing robotics systems, namely the cost of an integrated camera sensor & vision system that meets the bandwidth, processing and analysis requirements for many advanced robotics applications. An ideal collaborator would be developing either autonomous applications, such as driverless vehicles, robot systems, surveillance and security systems, inspection systems (or similar), or wearable camera systems for monitoring the activity of an individual from an egocentric perspective.

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Deep Reinforcement Learning for Dynamic Network Control

The University of Edinburgh, Dr. Paul Patras

Numerous real-world network control problems can be modelled as Markov Decision Processes, whereby agents select from a set of actions to interact with the target system. Through these actions, agents change the system's state and receive feedback via some reward functions. The project will develop a set of DQN algorithms for network control problems, including elastic computing and dynamic allocation of resources in mobile broadband networks.

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Predictive Modelling for Big Data Explorations: A Statistical Learning Approach

University of Glasgow, Prof. Peter Triantafillou

This PhD research will focus on a novel problem of predictive analytics over distributed data in the sense that it can be deployed in environments in which data owners and sources restrict access to their data (e.g., due to security/confidentiality reasons, or cost reasons) and allow only certain statistical summaries or aggregation functions to be constructed/executed over the data. Real application domains will enhance the applicability of the novel predictive modelling methodologies from this PhD research dealing with extracting and exploiting knowledge only from limited data access and cost/energy consuming predictive analytics tasks.

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Predictive Modelling for Big Data Explorations: A Statistical Learning Approach

University of Glasgow, Dr. Christos Anagnostopoulos

This PhD research focuses on an initiative for predictive analytics over distributed data in the sense that it can be deployed in environments in which data owners and sources restrict access to their data (e.g., due to security/confidentiality reasons or cost reasons) and allow only certain statistical summaries or aggregation functions to be constructed/executed over the data. This partnership will enhance the novel predictive modelling methodology from extracting and exploiting knowledge from limited data access and cost/energy consuming environments.

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