EPSRC-Eligible and EU applicants

Below, we list PhD topics for EPSRC-Eligible and EU applicants only. For overseas applicants see here

To be EPSRC-Eligible for a full award, an applicant must have no restrictions on how long they can stay in the UK and have been ordinarily resident in the UK for at least 3 years prior to the start of the studentship (with some further constraint regarding residence for education).

For further details see EPSRC Student Eligibility guide or contact Anne Murphy. 


Haptic Feedback System for Robots in Harsh Environments

Project number: 
To develop a haptic feedback system for robots operating in harsh environments (e.g., space and nuclear)
Prof. Tughrul Arslan
University of Edinburgh

Space and nuclear robots are exposed to significant amounts of radiation which can lead to errors and system-crippling failures with great losses in economy.

The limited human intervention for safe operation necessitates the need for a remotely connected haptic feedback system to aid human-robot interactions.

A dandelion-inspired drone: how to translate natural flyer ability of passive hovering to enhance drone endurance

Project number: 
This proposal builds on our recent discoveries, published in Nature (https://edin.ac/385DnRY) that reveal how the dandelion fruit can fly unpowered for hundreds of kilometres. In contrast, similar-size manmade drones have an endurance of few minutes. This PhD project aims to develop a deeper understanding of how the dandelion exploits wind gusts to remain airborne and to translate our insights from biology into the design of a new family of centimetre-scale drones with a step-change increase in the flight range and endurance.
Dr. Ignazio Maria Viola
University of Edinburgh

In the next decade, distributed sensor network systems made of insect-scale flying sensors will enable a step change in monitoring natural disasters and remote areas. They will contribute to protecting the environment by providing data on the contamination of physical and biological systems and on the impact of human activities. To date, a key limitation of this technology is that small drones such as the robobee can remain airborne only for few tens of minutes.

Fabrication of Soft Robotic Electronic Skin (E-Skin)

Project number: 
To design and develop stretchable e-skin for wearable and soft robotic applications, utilising novel digital manufacturing process.
Dr. Morteza Amjadi
Heriot-Watt University

Soft e-skins have recently attracted considerable research interest due to their applications in soft robotics, prosthetics, and artificial skins. Remarkable advances in materials science, nanotechnology, and biotechnology have led to the development of various e-skins capable of detecting different external stimuli, such as strain, pressure, temperature, hydration, and biomarkers.

Robots Safe and Secure by Construction

Project number: 
Verified implementation of machine-learning components of autonomous systems
Prof. Ekaterina Komendantskaya
Heriot-Watt University

Robotic applications spread to a variety of application domains, from autonomous cars and drones to domestic robots and  personal devices. Each application domain comes with a rich set of requirements such as legal policies, safety and security standards, company values, or simply public perception. They must be realised as verifiable properties of software and hardware. Consider the following policy: a self-driving car must never break the highway code.

Learning Dexterous Robotic Manipulation

Project number: 
Learning autonomous grasping and manipulation skills that are safe to be deployed in human environment with data-efficient deep reinforcement learning and human-robot skill transfer
Dr. Zhibin Li
University of Edinburgh


A large variety of robotic applications strongly involve handling various objects as the core process for task completion. To date, most of these jobs are still performed by people. Although some are automated by robots, those solutions primarily rely on pre-designed rules or tele-operation (limited operational time due to cognitive overload), which unavoidably limits the performance in changing environments. This project consists of multiple challenging research topics in robotic manipulation.

Project description

Deep Analysis: A Critical Enabler to Certifying Robotic and Autonomous Systems

Project number: 
Develop techniques that assist in certifying robotic and autonomous systems through a deep analysis at the level of requirements, problem worlds and specifications.
Prof. Andrew Ireland
Heriot-Watt University

Safety critical robotic and autonomous systems, such as Unmanned Air Vehicles (UAVs) that operate beyond visual line of sight, require the highest level of certification. Certifiers are concerned with how such systems behave within their environment – as defined by system wide requirements, e.g. compliance with the rules-of-the-air (i.e. SERA).   In contrast, software developer’s focus on specifications - how the system software should behave based upon operational modes and input signals. Many catastrophic system failures, e.g.

Multimodal fusion for large-scale 3D mapping

Project number: 
The project will explore the combination of 3D point clouds with imaging modalities (colour, hyperspectral images) via machine learning and computer graphics to improve the characterization of complex 3D scenes.
Dr. Yoann Altmann
Heriot-Watt University

Lidar point clouds have been widely used to segment large 3D scenes such as urban areas and vegetated regions (forests, crops, …), and to build elevation profiles. However, efficient point cloud analysis in the presence of complex scenes and partially transparent objects (e.g, forest canopy) is still an unsolved challenge.

Wearable and Stretchable Stain/Tactile Sensors for Soft Robotic Applications

Project number: 
To design and develop stretchable optomechanical sensors, and investigate their integration with soft gripper robots towards soft robots with feedback sensation
Dr. Morteza Amjadi
Heriot-Watt University

Wearable sensor technologies have recently attracted tremendous attention due to their potential applications in soft robotics, human motion detection, prosthetics, and personalized healthcare monitoring. Remarkable advances in materials science, nanotechnology, and biotechnology have led to the development of various wearable and stretchable sensors. For example, researchers including us have developed resistive and capacitive-type strain and pressure sensors and demonstrated their use in soft robotics, tactile sensing and perception, and human body motion detection.

Endoscopic Robot for Distal Lung Sampling

Project number: 
Develop image-guided control algorithms for autonomous pulmonary sampling using a robotic endoscopic platform.
Dr. Mohsen Khadem
University of Edinburgh

Pulmonary infiltrates such as pus, blood, or protein, which lingers within the parenchyma of the lungs are the leading cause of pneumonia, tuberculosis.  Pulmonary infiltrates in mechanically ventilated (MV) critically ill patients in the intensive care unit (ICU) are a major diagnostic challenge and due to the poor sampling methods available.

Rethinking Deep Learning on Remote Smart Sensors

Project number: 
Develop new neural network compression mechanisms to accelerate neural networks on low powered FPGA and embedded GPU smart sensors
Dr. Robert Stewart
Heriot-Watt University

Neural networks for deep learning have been proven successful for many different domains, such as autonomous driving, conversational agents, autonomous robotics and computer vision. Neural network models are typically trained and executed on GPUs, but these have significant energy costs and lack portability needed for remote smart devices. FPGAs and embedded GPUs solve this problem, but cannot host large trained models. Thus, mechanisms to compress neural networks are needed to fit within hardware resource constraints without losing accuracy of AI inferences the model can make.