PhD Study

Remote patient monitoring using wearable devices and AI

Department of Computer Science

In this PhD project, you will conduct a comprehensive analysis of extensive datasets, such as RADAR-CNS and/or Mobilise-D, collected from large cohorts of individuals monitored remotely for over two years. These datasets encompass a diverse range of physiological and behavioural parameters, including heart rate, sleep patterns, physical activity, GPS location, and phone/app use. They represent the most extensive observational studies utilising wearable devices to date.

Deadline: Friday 19 April

Find out more information and apply here. 

Towards Responsible and Accessible Large Language Models

Department of Computer Science

This doctoral research initiative seeks to delve deeply into the complexities surrounding Large Language Models (LLMs) and to develop models and methodologies towards responsible, interpretable, and accessible LLMs.

Deadline: Friday 31 May

Find out more information and apply here. 

Multi-modal learning for in-process monitoring: Towards AI-driven manufacturing automation

Department of Automatic Control and Systems Engineering

The University of Sheffield, in conjunction with the EPSRC-funded Made Smarter Connected Factories, is pleased to announce the availability of a funded Ph.D. scholarship. This scholarship is dedicated to research in the field of multi-modal learning, particularly for industrial process monitoring and control.

Deadline: Friday 31 May

Find out more information and apply here. 

Statistics and AI for Engineering and Smart Manufacturing

School of Mathematics and Statistics

We are thrilled to announce an exceptional PhD opportunity in the dynamic fields of Statistics and Artificial Intelligence, specifically tailored for Engineering and Smart Manufacturing. This project is designed for candidates eager to explore and expand the realms of statistical analysis and reliable AI in industrial pipelines, with a special emphasis on smart manufacturing and electronic design automation (EDA).

Applications accepted all year round.

Find out more information and apply here. 

Developing AI Controlled Granulation Process for Formulated Chemicals

Department of Chemical & Biological Engineering

Seeking candidates for a project in the chemical and pharmaceutical sector focusing on seeded granulation, a novel process that enhances the uniformity and strength of granular products. Utilising Industry 4.0 technologies like machine learning and AI, the project aims to develop digital sensors for real-time process optimisation. Experimental work will explore the mechanism of seeded granulation, with recommended use of EDEM or gProms software and Python for process analysis. The project promises to impact high-value markets including pharmaceuticals, nutraceuticals, and detergents, offering significant commercial opportunities.

Applications accepted all year round.

Find out more information and apply here. 

Artificial intelligence and machine learning methods for model discovery in the social sciences

Department of Automatic Control and Systems Engineering

Many theories have been proposed for how problematic social phenomena - such as segregation, inequality, and clustering of unhealthy behaviours - emerge and change over time. But which theories are useful for understanding these phenomena and devising effective interventions to change them? This project will develop computationally efficient model discovery methods to enable a search for explanatory multi-level agent-based models that can be calibrated to - and validated against - such empirical phenomena.

Applications accepted all year round.

Find out more information and apply here. 

Online discussion; augmenting argumentation with chatbots

Department of Psychology

Argumentation - the systematic exchange of reasoning supporting or undermining an idea - enhances communication between individuals. Unreliable chains of thought are weeded out, reliable ones survive. Striking evidence for this is that reasonings tasks which provoke systematic errors when considered by individuals choice can be solved correctly by small groups, if they are given time for discussion. Chatbots with natural language processing create an opportunity to have artificial agents interact with group deliberation, and make it more effective. In this way, the strengths of human and artificial intelligence can augment each other

Applications accepted all year round.

Find out more information and apply here.