Pigs vs AI: Can we use machine learning, as a substitute for human taphonomic facilities, to map and predict taphonomic change. Application deadline: 30. October 2023.
Applications are invited for a fully-funded PhD bursary to commence in February 2024.
The PhD is an exciting collaborative project between the School of Criminology and Criminal Justice and the School of Computing. It will be based in the Faculty of Humanities and Social Sciences, and will be supervised by Dr Kat Brown, Dr Helen McGonigal and Dr Ella Haig.
In this project, you will explore the array of existing animal and human decomposition datasets for the creation of a new artificial neural network model (ANNs) for estimation of time since death. By conducting large scale porcine studies, this model will be tested, and its potential assessed for use in forensic investigation, with the aim of producing recommendations for new taphonomy research.
Candidates applying for this project may be eligible to compete for one of a small number of bursaries available. Successful applicants will receive a bursary to cover tuition fees at the UK/EU/Overseas rate for three years (full time), an annual stipend in line with UKRI rates (£18,622 for 2023-24) and a contribution of £2,000 towards consumables, conference, project or training costs.
Your work on this project could involve:
- Working with School of Computing Colleagues, to investigate whether, using ANNs, animal data can accurately predict and be applied to human decomposition data and case studies.
- Conducting your own research comparing human decomposition (at a European or another partner facility) and pig decomposition at UoP, and apply ANNs to this novel dataset.
- Reporting and presenting recommendations surrounding the need for large taphonomic research studies in the UK, and promote the link between understanding developed in research to professional practice and vice versa.
Although animal analogues are commonly used for taphonomic research, studies that validate the use of data extrapolation to human decomposition and subsequent death reconstruction are minimal.
You will aim to establish whether animal data can be accurately extrapolated to human data, through a multidisciplinary and comparative approach. Moreover, you will explore the use of artificial intelligence (AI) and machine learning (ML) to model and predict pig and human data in a range of conditions, and potentially help contribute to post-mortem interval estimation (PMI). The use of artificial neural networks (ANNs) allows for the analysis of non-linear data that contains a significant amount of ‘noise’, making them well suited to the application to PMI estimation.
You will have a large focus on developing existing and collaborations with taphonomy facilities and research groups, globally. Mixed methods research will be employed, including quantitative data analysis and practical research in the laboratory and at our decomposition sites. Multiple scientific methods will be used to explore many taphonomic factors, including entomology, spectroscopy, botany, microbiology and geology.
Through answering the research questions raised in this project and determining whether you can extrapolate enough data from animal research and AI/ML approaches, you will contribute to the debate on the need for large taphonomy facilities in the UK.
You'll need a first degree from an internationally recognised university (minimum upper second class or equivalent) or a Master’s degree in Forensic Science/Investigation (or similar). In exceptional cases, we may consider equivalent professional experience and/or qualifications. You’ll need English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.
You must be prepared to work on campus in Portsmouth, with the potential for global travel to other research sites. You must be prepared to travel independently to visit taphonomic research sites in the UK. You will be required to teach up to 6 hours per week during term time, which will be supported with appropriate training and qualifications.
We particularly welcome applications from students from the following groups, as the Office for Students has identified that they are currently underrepresented in Higher Education:
Students from areas of low higher education participation, low household income or low socioeconomic status;
- Black, Asian and minoritized ethnic students;
- Students from Gypsy, Roma and Traveller communities;
- Disabled students;
- Carers and care leavers;
- People estranged from their families;
- Children from military families;
How to apply
We’d encourage you to contact Dr Kat Brown (Katherine.email@example.com) to discuss your interest before you apply, quoting the project code.
When you are ready to apply, you can use our online application form. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV.
If you want to be considered for this funded PhD opportunity you must quote project code SCCJ8431023 when applying.