Previous PGR seed corn projects
See a list of all our previous PGR seed corn projects we have funded.
Follow on seed corn projects
See our list of follow-on seed corn projects we have funded.
Staff seed corn projects
We have been able to fund pilot projects for staff at the University of Bristol since 2016. See below for a list of previous projects we have been able to fund.
2022/2023 | |
|---|---|
| Cheryl McQuire | |
| Engineering Art: Algorithmic personalisation of visual preference using eye-tracking and deep learning | Lazlo Talas |
| SS Great Britain: data-driven modelling for advanced management | Alberto Gambaruto |
| The interplay between pain medication, workplace productivity, and well-being: seeking ground truth and refining Big Data analytic methods | Neo Poon |
| Unlocking bigweb archives: a toolto learn about new economic activitiesover space and time | Emmanouil Tranos |
| Assessing the recombinogenic potential of novel bacterial lineages: Towards an early warning system for problem pathogens | Sion Bayliss |
| Empowering Power Electronics with AI: Partial Discharge Detection and Classification using Machine Learning | Wenzhi Zhou |
| A new phylogenomic framework for studying the origin, evolution and diversification of polyketide biosynthesis | Tom Williams |
| Developing an Integrated and Intelligent Algo-trading System | Jin Zheng |
2020/2021 | |
|---|---|
| What is the best relationship between humans and AI? Investigating researcher perceptions of AI through immersive experience | Richard Owen |
| Secure machine learning on sensitive ground truth data held by UK birth cohorts | Valerio Maggio |
| Non-invasive imaging of the eye to predict Alzheimer’s disease | Denize Atan |
| A Digital Twin Enabler for the Clifton Suspension Bridge: an open-interface structural model | Sam Gunner |
| The ‘symbolic annihilation of women’ in primary school literature” | Chris McWilliams |
| Evaluating fairness, bias and equality in Artificial Intelligence for skin disease | Kevon Parmeser |
| Convolutional Neural Networks for Environmental monitoring | Christopher Williamson |
| Pandemics and ‘infodemics’: the nature, extent and reach of public health misinformation on social media during the COVID-19 pandemic | Cheryl McQuire |
| Effects of adolescent physical activity on physical and mental health in adulthood: novel multivariate pattern analysis of the intensity spectrum | Ahmed Elkaheen |
| Pilot Assessment of Cancer Risk After SARS-COV-19 | Pau Erola |
| Optimising the allocation and distribution of COVID-19 vaccines in the UK | Xin Fei, Xiaojun Wang |
| Earth’s climate at your fingertips: connecting multidisciplinary environmental sciences and the public through interactive data exploration | Sebastian Steinig |
| Research searcher | Benjamin Elsworth |
2019/2020 | |
|---|---|
| Mood music: using Spotify to infer wellbeing | Oliver Davis |
| Digital Humanities meets Medieval Financial Records: The Receipt Rolls of the Irish Exchequer | Brendan Smith |
| Automating food aggregation for nutrition and health research | Zoi Toumpakari |
| Elements of free text used in decision making: an exemplar from death reviewsin prostate cancer and learning disabilities | Avon Huxor |
| Decoding pain: development of a clinical tool to enable real-time data visualisation and analysis of human pain nerve activity | Jim Dunham |
| Super-charging single-cell imagingpathology | Ranjeet Bhamber |
| Mental health and educational achievement in two national contexts: A machine learning approach | Liz Washbrook |
| Chemspeed data capture and curation | Ella Gale |
| Machine learning assisted polymer design | Pierangelo Gobbo |
| Bristol Explores Brunel's Temporal Social Network' | James Boyd |
| Mapping Oliver Messel | Elaine McGirr |
2017/2018 | |
|---|---|
| Immune response networks in wild mice | Naoki Masuda |
| Social movement differences and risk of psychosis: Data-driven approaches for mental health diagnostics | Sarah Sullivan |
| Developing computational tools for analysis of exome and genome data in highly phenotyped rare disease cohorts | Agnieszka Bierzynska |
| EPIC-KITCHENS 2018 Dataset – JGI Sponsorship for Large-Scale Dataset (Publishing and Promotion) | Dima Damen |
| Enabling advanced analytics for all users of the proteomics facility | Andrew Dowsey |
| Exploiting big data for greenhouse gas emissions estimation using INLA | Luke Western |
| Fernando Sanchez-Vizcaino | |
| Development of machine learning algorithms for antimicrobial resistance testing | Massimo Antognozzi |