Overseas Applicants

The following project topics are open to all applicants including Overseas.

For other topics open to EPSRC-Eligible/EU applicants only see here.  Contact Anne Murphy if you have any questions.

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

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.

Human-Machine Collaboration for Efficient Spatio-Temporal Biodiversity Monitoring

Project number: 
Efficient estimation of changes in spatio-temporal distributions of wildlife via active data collection
Dr. Oisin Mac Aodha
University of Edinburgh

There is a critical need for robust and accurate tools to scale up biodiversity monitoring and to manage the impact of anthropogenic change. For example, the monitoring of individual species that are particularly sensitive to habitat conversion and climate change can act as an important indicator of ecosystem health. Existing approaches for collecting data on individual species in the wild have traditionally been based on manual surveys performed by human experts.

Deep Learning of Object Shape from Video

Project number: 
Learning visual representations of objects that encode both shape and appearance
Dr. Oisin Mac Aodha
University of Edinburgh

The shape and 3D structure of the world provides us with rich signal that enables us to interact with objects and to navigate in novel and dynamic environments. Despite the importance of this information to human visual reasoning it still remains largely underutilized in modern deep learning based semantic image understanding pipelines commonly used in robotics. For example, current best performing approaches for object classification in images are predominantly based on heavily supervised feedforward convolutional neural networks.

Autonomous Driving in Urban Environments

Project number: 
Develop and evaluate algorithms for autonomous driving in urban environments
Dr. Stefano Albrecht
University of Edinburgh

The coming decades will see the creation of fully autonomous vehicles (AVs) capable of driving without human intervention. Among the expected benefits of AVs are a significant reduction in traffic incidents, congestion, and pollution, while dramatically improving cost-efficiency.

Robust and Explainable Machine Learning for FinTech Applications

Project number: 
To develop and compare Gaussian Process models with Deep Neural Networks to provide explainable and quantifiable Machine Learning for FinTech applications.
Prof. Mike Chantler
Heriot-Watt University

Deep Neural Network (DNN) technologies coupled with GPU type hardware provide practical methods for learning complex functions from vast datasets.  However, their architectures are often developed using trial and error approaches and the resulting systems normally provide ‘black box’ solutions containing many millions of learnt but abstract parameters. They are therefore extremely difficult to interpret and understand, and their accuracy and certainty of prediction, or classification, are normally not known.

Analysis of Controlled Stochastic Sampling for training RL Agents for Robotics Tasks

Project number: 
For tasks where path-planning of real robots is guided via a simulation of a virtual agent, this project aims to understand the role and impact of the randomisation scheme on the efficiency and generalisability of the agent.
Dr. Kartic Subr
University of Edinburgh

Data-driven machine learning techniques are popularly used in the field of robotics to inform autonomous decision-making and to perform control or path-planning. Supervised learning and reinforcement learning have been shown to be particularly amenable to canonical tasks that are integral to robotics applications. However, these techniques rely on data in the form of action-label (supervised), action-value (regression) or action-reward (RL) pairs, where the action is a path (or some other) execution by a real robot. e.g.

Curiosity-driven Learning for Visual Understanding

Project number: 
The goal of this project is to enable learning systems with the ability to have curiosity, based on their ability to already understand the world around them, to make learning faster and more efficient.
Dr. Laura Sevilla-Lara
University of Edinburgh

Curiosity guides humans to learn efficiently. It incentivizes us to spend more energy and time examining new, unexpected things, and to disregard those we fully understand already, to make our learning more efficient. Much of the vision learning that is done today is passive: learning systems are exposed to large amounts of training data, and learn from each sample multiple times, regardless of their current ability to recognize them at the time. This makes the process slow, specially given the increasingly large number of samples on datasets.