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  Strengthening the interpretation of explosive-related analytical data in forensic science (ref: DSTL20/HLS/APP/GALLIDABINO)


   Faculty of Health and Life Sciences

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

About the Project

Explosive materials, such as trinitrotoluene (TNT) or triacetone triperoxide (TATP), are used to commit a number of different crimes and the analysis of related residues typically plays a crucial role in investigation. For this purpose, highly sensitive methods currently exist (e.g., LC-HRMS). Assigning a meaning to the observed scientific findings, however, is still a very challenging task for forensic laboratories. The main reasons for this are the lack of both necessary fundamental data and an adequate interpretation framework.
This broad inter-disciplinary project aims to tackle both these problems. Starting from current practices and protocols used in the UK for explosive analysis, the student will collect fundamental data about the repeatability, selectivity, prevalence, occurrence and background levels of explosive-related markers. This will be carried out through a series of surveys covering different locations and cities around the country. Then, in collaboration with an internationally recognised Forensic Service Provider in the field of explosives investigation, they will analyse and break down the reasoning patterns underlying evidence evaluation in criminal cases, in order to understand how collected explosive-related data are typically used by practitioners to form their opinions. Finally, the student will drive the design of a bespoke, comprehensive and robust interpretation framework to combine all the acquired knowledge and assign evidential values to new findings. This framework will constitute the blueprint for a novel expert system to support practitioners with evaluative assessment and expert witness testimony in future cases.
The project will be in collaboration with the Ministry of Defence; in particular, with the Forensic Explosive Laboratory (FEL) of the Defence Science and Technology Laboratory (Dstl) and will involve extensive cooperation with casework practitioners. The student will be trained in the forensic analysis of explosive residues (including sample preparation and LC-HRMS), evidence interpretation and statistical/probabilistic modelling of forensic data. A placement period at FEL is planned, in order for the student to familiarise themselves with accredited and routine procedures.

Eligibility and How to Apply:

Please note eligibility requirement:
• UK nationality; no dual nationality will be retained.
• 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.


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: Applications should include a covering letter that includes a short summary (500 words max.) of the reasons you consider yourself suited to the project. Applications that do not include the advert reference (e.g. DSTL20/…) will not be considered.

Deadline for applications: Thursday 17th December 2020
Interviews: January 2021
Start Date: 1st March 2021

Northumbria University takes pride in, and values, the quality and diversity of our staff. We welcome applications from all members of the community. The University holds an Athena SWAN Bronze award in recognition of our commitment to improving employment practices for the advancement of gender equality.


Funding Notes

The studentship is available to Home (UK national) students with a full stipend, paid for three years at RCUK rates (for 2020/21, this is £15,285 pa) and full Home Fees.

References

Gallidabino, M.D., Barron, L.P., Weyermann, C., & Romolo, F.S. (2019). Quantitative profile-profile relationship (QPPR) modelling: a novel machine learning approach to predict and associate chemical characteristics of unspent ammunition from gunshot residue (GSR). Analyst, 144(4), 1128ss. DOI: 10.1039/C8AN01841C

Gallidabino, M.D., Irlam, R.C., Salt, M.C., O’Donnell, M., Beardah, M.S., & Barron, L.P (2019). Targeted and non-targeted forensic profiling of inorganic explosives using gradient ethanol-enhanced anion exchange chromatography – high resolution (Orbitrap) mass spectrometry (IC-HRMS). Analytica Chimica Acta, 1072, 1-14. DOI: 10.1016/j.aca.2019.04.048

Sironi, E., Taroni, F., Baldinotti, C., Nardi, C., Norelli, G.-A., Gallidabino, M.D., & Pinchi, W. (2018). Age estimation by assessment of pulp chamber volume: a Bayesian network for the evaluation of dental evidence. International Journal of Legal Medicine, 132(4), 1125-1138. DOI: 10.1007/s00414-017-1733-0

Gallidabino, M.D., Weyermann, C., Romolo, F.S., & Taroni, F. (2013). Estimating the time since discharge of spent cartridges: a logical approach for interpreting the evidence. Science & Justice, 53(1), 41-48. DOI: 10.1016/j.scijus.2011.12.004


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