ABOUT THE DIN

Development of an AI detection algorithm for ionizing radiation sensors

About the Company

Defence Science and Technology Group (DSTG) is the Australian Government’s lead agency dedicated to providing science and technology support for the country’s defence and security needs.

Project Objective

Develop a machine learning algorithm for the detection and identification of radioactive material based on spectral data from mobile radiation detection systems.

Fast and accurate detection and identification of radiation sources are of significant importance to operating safely and effectively in radiological threat environments. Radiation detectors currently employing traditional detection algorithms may not be optimally performant when operated on fast-moving platforms in urban environments. Machine learning algorithms could be employed post-hoc to collected data to better interpret data and inform Defence activities in radiological threat environments.

 Project Tasks

• Download and explore the dataset. Depending on the intern’s level of familiarity with radiation detectors, this may also include familiarisation with relevant detector physics.
• Conduct a literature review on relevant machine learning architectures for radiation signal detection.
• Select or develop an appropriate architecture and apply it to the training data subsets.
• Assess the performance of the AI algorithm by comparison to testing data subset and ground truth data provided.
• Provide a summary of the work conducted in a report. Include all software artefacts (code, requirements, etc.).

Intern Skills

• Software development interests, familiarity with AI development tools in Python. Radiation physics and detection knowledge would be helpful but not essential.
• An interest in physics, physics-informed machine learning and software development. Highly motived.
• Able to work remotely under limited supervision.
• Curious mindset. Active participation in relevant internship meetings.

Application

Students should fill out an online application form and submit their supporting documents (listed below) to info@defenceinnovationnetwork.com by 13 September 2024.

• Curriculum Vitae
• Motivation Letter
• Supervisor Support Letter
• Proof of Australian citizenship or permanent residence
  (e.g. passport, birth certificate, citizenship or PR certificate)