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  Profiling of gunshot and explosive residues: development of next-generation analytical and chemometric tools for enhancing possibilities in forensic science (RDF19/HLS/AS/GALLIDABINO)


   Faculty of Health and Life Sciences

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  Dr M Gallidabino  No more applications being accepted  Funded PhD Project (European/UK Students Only)

About the Project

Residues of energetic materials, such as gunshot residues and explosives, are common forensic evidence types that assist the investigation of shooting and/or bombing events. Despite the inherent abundance of chemical information that can be acquired from their analysis, however, just a small proportion is currently exploited and usually only for the purpose of identifying the type of original material. Establishing a common source between seizures at different locations and/or inferring the precise activity that generated them would provide helpful additional intelligence. Unfortunately, this remains very challenging with current scientific techniques, leaving critical decisions in the hands of non-expert investigators.

This project aims to improve this situation and, thus, enhance the role of the forensic scientist in the investigation of crimes involving such trace types, as is already the case in other fields of criminalistics. Recent literature, in particular, has shown that the application of multi-residue and/or non-targeted methods coupled to next-generation chemometrics can efficiently assist answering complex questions about source and activity of energetic residues. The purpose of this project is thus to explore the potential of these new approaches and provide solutions that can be implemented in an operational forensic context. New comprehensive analytical and sampling techniques will be developed for the analysis of residues on a range of surfaces. Then, chemometric tools based on machine learning and artificial intelligence will be applied to the analysis of the data. This will allow computer modelling of underlying transfer phenomena and, consequently, the extraction of enhanced forensic evidence and/or intelligence. In particular, in silico profiling and omics-inspired approaches will be investigated. The future benefits will be broad, with potential opportunities for extension to other fields (e.g., human and environmental toxicology). The project will involve extensive collaboration with international and national partners in academia, industry and relevant governmental agencies.

Eligibility and How to Apply:
Please note eligibility requirement:
• Academic excellence of the proposed student i.e. 2:1 (or equivalent GPA from non-UK universities [preference for 1st class honours]); or a Masters (preference for Merit or above); or APEL evidence of substantial practitioner achievement.
• Appropriate IELTS score, if required.
• Applicants cannot apply for this funding if currently engaged in Doctoral study at Northumbria or elsewhere.

For further details of how to apply, entry requirements and the application form, see
https://www.northumbria.ac.uk/research/postgraduate-research-degrees/how-to-apply/

Please note: All applications must include a covering letter (up to 1000 words maximum) including why you are interested in this PhD, a summary of the relevant experience you can bring to this project and of your understanding of this subject area with relevant references (beyond the information already provided in the advert).

Deadline for applications: Friday 25 January 2019

Start Date: 1 October 2019

Northumbria University is an equal opportunities provider and in welcoming applications for studentships from all sectors of the community we strongly encourage applications from women and under-represented groups.

Faculty: Health and Life Sciences
Department: Applied Sciences
Principal Supervisor: Dr Matteo Gallidabino (1st), Prof. John Dean (2nd)


Funding Notes

The studentship is available to Home/EU students where a full stipend, paid for three years at RCUK rates (for 2018/19, this is £14,777 pa) and full fees.

References

Gallidabino, M.D., Barron, L.P., Weyermann, C., & Romolo, F.S. Quantitative profile-profile relationship (QPPR) modelling: a novel machine learning approach to predict and associate chemical characteristics of unspent ammunition from gunshot residue (GSR) (2019). Analyst, in press.

Gallidabino, M., Romolo, F.S., & Weyermann, C (2017). Time since discharge of 9 mm cartridges by headspace analysis, part 2: ageing study and estimation of the time since discharge using multivariate regression. Forensic Science International, 272, 171-183. DOI: 10.1016/j.forsciint.2016.12.027

Gallidabino, M., Romolo, F.S., & Weyermann, C (2017). Time since discharge of 9 mm cartridges by headspace analysis, part 1: comprehensive optimisation and validation of a headspace sorptive extraction (HSSE) method. Forensic Science International, 272, 159-170. DOI: 10.1016/j.forsciint.2016.12.029

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