Automated use of 3D data in forensic odontology identification
Call for applications for a fully financed PhD fellowship (readvertisement)
The scene is a future catastrophe, disaster or terror attack with many casualties. The victims need to be identified unambiguously and quickly. This important work is carried out by highly specialized Disaster Victim Identification (DVI) teams, who to a great extend exploit forensic odontology identification (FOd) – defined by INTERPOL as one of the three primary identifiers in identification.
Project description
Till now, FOd has mostly been based on 2D data, including written dental records and X-rays recorded in connection with diagnostics and dental work. However, the implementation of new 3D technologies in dentistry together with an overall improvement in dental health, meaning that the amount of specific individual dental 2D data become less, calls for innovation within FOd. Implementation of post mortem 3D data documentation and ante mortem-post mortem automated comparison procedures in FOd is expected to be of great advantage, since the level of detail is very high.
The project aims to explore and innovate automated use of 3D photo scan data in forensic odontology identification. Automating the process and focusing on making it operational is expected to optimize the identification of victims in the future. The project includes three suggested studies:
- Study 1: Exploring the matching potential for 3D photo scan data; partial versus ideal/full data. (Segmentation, superimposition, advanced data analysis.)
- Study 2: Optimizing and automating the process from retrieval of 3D data to matching results. (Retrieval and uploading, alignment (machine learning), superimposition, advanced data analysis; software development.)
- Study 3: Exploring the potential in automation in matching experimental post-traumatization clinical 3D dental data with pre-traumatization data. (Segmentation, superimposition, advanced data analysis.)
The studies are planned to include a stay abroad, at the associated collaborator in Scotland, exploring a broader aspect of the 3D potential in disaster victim identification.
The PhD student will be part of a team comprising expertise in forensic odontology, bioinformatics, biomedical imaging and good clinical practice. During the PhD-studies, the applicant will acquire skills within advanced data analysis, segmentation, superimposition and machine learning, and obtain insight in the field of forensic odontology.
Funding for the PhD project, including the PhD Student salary, has been obtained from AUFF NOVA, Aarhus Universitets Forskningsfond (Aarhus University Research Foundation).
IF you find this project exciting and are interested in working with advanced data analysis including 3D modelling - be sure to apply for the position.
Further information
Day-to-day supervisor: Assistant Professor Line Staun Larsen, Department of Forensic Medicine and Department of Dentistry and Oral Health, Aarhus University.
Main supervisor: Associate Professor Palle Villesen, Bioinformatics Research Centre (BiRC), Department of Clinical Medicine, Aarhus University.
Affiliation: Department of Forensic Medicine, Aarhus University.
Please contact Assistant Professor Line Staun Larsen, line.staun@dent.au.dk, for further information.
Qualifications
Candidates must have a bachelor’s or master’s degree in bioinformatics, computer science, health science or related fields and competences within computer science and possess a genuine interest in working with 3D modelling. Spoken fluent Danish and master English corresponding to CEFR C1-level is a requirement.
How to apply
Please submit your application via this link.
Application deadline is 15 June 2022 23:59 CET.
Preferred starting date is 1 September 2022.
For information about application requirements and mandatory attachments, please see our application guide.
All interested candidates are encouraged to apply, regardless of their personal background. Salary and terms of employment are in accordance with applicable collective agreement.
