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.

Vision based Mobile Autonomy through Object-Level Scene Understanding and Robust Visual SLAM

Project number: 
600005
The goal of this project is to develop vision based algorithms for long-term mobile autonomy in dynamic environments, leveraging object-level scene understanding, multi-sensor fusion and visual SLAM.
Dr. Sen Wang
Heriot-Watt University

AnKobot is sponsoring an exciting PhD project in the field of mobile autonomy using visual Simultaneous Localization and Mapping (SLAM), semantic scene understanding and computer vision.

Robust and Explainable Machine Learning for FinTech Applications

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.

Controllable neural text generation for safe human-machine interactions

Project number: 
400002
The goal of this research is to develop novel neural text generation models, which can guarantee semantic completeness and thus enable safe human-machine interactions.
Prof. Verena Rieser
Heriot-Watt University

Natural Language Generation (NLG) is the task of translating machine-readable representations and data into human language, and thus vital for accountability in safe human-machine collaboration. Neural Network architectures for NLG are promising since they able to capture linguistic knowledge through latent representations using raw input data, and hence have the benefit of simplifying the design of systems by avoiding costly manual engineering of features, with the potential of more easily scaling to new data and domains.

New number formats for faster deep learning

Project number: 
400001
Exploring the use of POSIT numbers for adaptive precision schemes in deep learning algorithms.
Prof. Sven-Bodo Scholz
Heriot-Watt University

The training phase in Deep Learning is very compute and data intensive and, therefore, the efficiency of the training phase typically restricts the quality of results that can be achieved within a given time frame.

Many learning algorithms are dominated by the speed in which data can be brought to the CPU, i/e., by the memory bandwidth of the executing hardware. Consequently, techniques that are based on reduced precision number representations have been shown to produce faster results without a significant loss in the quality of results.

Incremental Learning for Robotic Lifelong Learning

Project number: 
240010
To develop incremental learning techniques that support lifelong learning in robotics.
Prof. Nick Taylor
Heriot-Watt University

When robots are co-located with people then safe interaction between them requires that the robot has reliable and up to date information about its environment and the locations of all persons and objects within it. The safety of any actions undertaken by the robot need to be assessed against the current state of its world and so the robot’s model of the world must be regularly updated.

Accountable Conversational AI for Human-Robot Collaboration

Project number: 
240008
To extend & develop models for (Visually) Grounded Human-Machine Collaboration in conversation, including mechanisms for detection & resolution of ambiguity & vagueness to mitigate risk of miscommunication.
Dr. Arash Eshghi
Heriot-Watt University

Ambiguity & Vagueness are pervasive in human  conversation; and their detection and resolution via clarificational dialogue is key to collaborative task success. State-of-the-art HRI cannot handle this, thus increasing risk of miscommunication in human-machine collaboration, especially in safety-critical environments.

Safe Human-Robot Teaming through User-Adaptive Interaction

Project number: 
240007
The goal of this project is to enable robots to safely and effectively collaborate with humans in teams through human-robot interaction by adapting to the social and communication skills level of the user.
Prof. Helen Hastie
Heriot-Watt University

For humans to collaborate effectively and safely on shared tasks in human-machine teams, they need to be able to develop a trusting, working relationship. To do this, partners need to have a mutual understanding of the task and understand what the other party can and can’t do.

Safe Human-Robot Teaming through Visually Grounded Interaction

Project number: 
240006
The goal of this project is to enable robots to safely and effectively collaborate with humans in teams through grounded human-robot interaction.
Prof. Helen Hastie
Heriot-Watt University

For humans to collaborate effectively and safely on shared tasks in human-machine teams, they need to be able to develop a trusting, working relationship. To do this, partners need to have a mutual understanding of the world and the task at hand. As robotic systems become more human-like and autonomous, this relationship with humans becomes more important; because once established, human-machine teams will need to be more efficient and more robust to be able to safely handle problematic situations, for example, when failure occurs, be it on the system or human side.