PhD Scholarships
2026
PhD scholarships for September 2026 start: the application is open for a few PhD scholarships for September 2026 start (due 7 Jan 2026, open to both UK and international students): https://jobs.nottingham.ac.uk/Vacancy.aspx?ref=SCI3046
This is quite competitive and the successful applicant usually has a couple of good publications already.
Nowadays I am mostly working on Human-Centred Agentic Science (human-agent collaboration to speed up scientific discovery). See the project page for more details.
2025
- PhD scholarship on using LLM to analyse ancient Roman tablet: open to international students and deadline 02 June 2025. This is a collaboration with the British Museum.
Fully funded PhD scholarship for students starting Sep 2025 (UK students only, deadline: 06 May 2025). The two projects I am involved in:Human-AI collaboration for medical data mapping using Large-Language Models recommendations, decision explanations and feedback (co-supervised by staff from Medicine & Health Sciences and Engineering)Large language model-aided ontology-based knowledge modelling in built environment contract management (co-supervised by staff from Engineering)
Fully funded PhD scholarship for students starting Sep 2025 (deadline: 06 Apr 2025, UK and international students): 10 in total, no specific topic (University page)
2023
Fully funded PhD scholarship (deadline: 31 March 2023, UK students only): Guided Image Generation for Artists (GIGA) – Making Deep Learning-Based Image Generators Accessible to Artists(University page, FindAPhD)Fully funded PhD scholarship (deadline: 12 Feb 2023, UK and international students): 10 in total, no specific topic(University page)Fully funded PhD scholarship (deadline: 6 Feb 2023, UK students only): Understanding and responding to youth violence – blending data science with lived experience(University page)
Projects (looking for students)
- Vitality 2: Chat with Your Papers
Key technologies: Front end: React or Flutter. Back end: vector database (chroma). Machine learning: LLM, RAG (such as RAGFlow), agent orchestration framework (such as LangChain/LangGraph), and MCP. You can see all the source code here. Background There are many types of qualitative analyses for academic publications. These are analyses that do not involve numerical computation… Read more: Vitality 2: Chat with Your Papers - Machine Learning for Automated Trading
Key technologies: Front end: React or Flutter. Back end: (online) relational database, especially for time-series data. Machine learning: deep learning, anomaly detection, model tuning and performance tracking (such as MLFlow), LLM, and agent orchestration framework (such as LangChain/LangGraph). The source code is here (for front end). Background The main idea is to use machine learning… Read more: Machine Learning for Automated Trading - Human-Centred Agentic Science: New Paradigm for Scientific Discovery
Key technologies: Front end: React or Flutter and JavaScript-based visualisation such as d3.js and Observable. Back end: vector database (chroma). Machine learning: prompt engineering, agent orchestration framework (such as LangChain/LangGraph), and MCP. Background AI has been behind some of the recent scientific breakthroughs. One of the most prominent examples is the Nobel Prize work of… Read more: Human-Centred Agentic Science: New Paradigm for Scientific Discovery
Previous Projects (no longer looking for students)
- Human-AI Collaboration – LLM for Qualitative Analysis
The recent breakthrough in Large Language Models (LLMs), such as chatGPT, and generative models, such as Stable Diffusion, can be tremendously valuable in supporting analysis and creative tasks. While powerful, such models can be difficult to use, especially for domain experts, such as qualitative researchers and visual artists, who are not experts in machine learning.… Read more: Human-AI Collaboration – LLM for Qualitative Analysis - Human-AI Collaboration: Generative Models for Artistic Creation
The recent breakthrough in Large Language Models (LLMs), such as chatGPT, and generative models, such as Stable Diffusion, can be tremendously valuable in supporting analysis and creative tasks. While powerful, such models can be difficult to use, especially for domain experts, such as qualitative researchers and visual artists, who are not experts in machine learning.… Read more: Human-AI Collaboration: Generative Models for Artistic Creation - Visual Analytic for Sensemaking
What is Visual Analytics? Visual Analytics is closely related to Data Visualisation, which is also known as Information Visualisation, that helps users discover insights (from data) by presenting it visually. This takes advantage of the visual cognition system (brain is part of it), which is a very powerful computer for detecting certain patterns (we became… Read more: Visual Analytic for Sensemaking - Provenance for Sensemaking
What is Provenance? The word provenance originally was mainly used for art work, refers to ‘the history of ownership of a valued object or work of art or literature’. For a painting, this includes the information of who painted it when and how the painted changed hands over time. More recently, the concept is expanded… Read more: Provenance for Sensemaking - Data Science Provenance
Background While there are some efforts to automate the process of build machine learning model (commonly known as ‘AutoML‘), there are many tasks in the different stages of a data science pipeline (see the diagram below) cannot be fully automated. For example, for ‘Data Wrangling’ a decisions have to be made on which dataset to… Read more: Data Science Provenance
Human-AI Collaboration 