Explore a range of aeronautical engineering research internships to complete as part of your degree during the semester break.
The following internships are due to take place across the Winter Break.
Applications open 1 April and close at midnight on 27 April 2026.
Supervisor: Prof Dries Verstraete
Eligibility: WAM>75 and Undergraduate candidates must have already completed at least 96 credit points towards their undergraduate degree at the time of application.
Project Description:
People living in regional and rural Australia face unique challenges due to their (isolated) geographical location and often have poorer health outcomes than people living in metropolitan areas. Rural and remote areas have double the number of preventable hospitalisations and two-and-a-half times more potentially avoidable deaths compared to metropolitan areas.
Drones could help improve health care services for remote Australians. However, current technology does not allow drones to cover the required distances while being sustainable and emissions free. A specialised medical drone is under development at the University of Sydney in partnership with ASAC Consultancy.
This research project aims to create an experimental database of motors, electronic speed controllers and propellers to enable the selection of the optimal propulsion system for this medical drone. Experimental data on powertrain components is extremely limited, and this project will provide critical data to extend the drone’s range.
Requirement to be on campus: Yes *dependent on government’s health advice.
Supervisors: Dr Morgan Li, Dr Mitch Bryson
Eligibility: Interests and experience in computer vision, image analysis, and programming
Project Description:
A long-running microplastic monitoring program in Australia samples deposits along the high-tide line and quantifies the number of microplastic particles in the collected material. This approach is intentionally simple and accessible, enabling participation by citizen scientists. However, in addition to the concentration of microplastics in the aquatic environment, several environmental factors, including wind and tidal conditions, affect the transport and accumulation of floating materials. To account for these variations, we need to assess the microplastic abundance against the total volume of debris transport, represented by organic loads.
In this project, we will use the CoastSnap database, a citizen science initiative in which participants capture photographs of the coastline from fixed locations. By extracting indicators of organic material deposition on the beach from these images, we aim to examine whether there is a correlation between organic debris accumulation and microplastic loads, and to identify patterns in microplastic abundance over time.
Requirement to be on campus: No
Supervisors: Dr Morgan Li
Eligibility: Interests and experience in working with Arduino; familiar with Python
Project Description:
A long-running microplastic monitoring program in Australia samples deposits along the high-tide line and quantifies the number of microplastic particles in the collected material. This approach is intentionally simple and accessible, enabling participation by citizen scientists. However, in addition to the concentration of microplastics in the aquatic environment, several environmental factors, including wind and tidal conditions, affect the transport and accumulation of floating materials.
In this project, we aim to develop an Arduino-based data logger to monitor the wind speed and direction near the sampling site. The prototype will be calibrated against high-accuracy velocity measurement techniques in controlled laboratory settings. The data collected by the data logger will be used to assist the interpretation of microplastic load variations.
Requirement to be on campus: Yes *dependent on government’s health advice.
Supervisor: Dr Xiaofeng Wu
Eligibility: Aeronautical with Space Engineering, Mechanical with Space Engineering and Mechatronic with Space Engineering
Project Description:
This internship project focuses on the dynamics modelling and control of a multiple degree-of-freedom robotic satellite operating in a free-floating environment. The student will develop a high-fidelity dynamics model using a physics-informed neural network (PINN), enabling accurate representation of complex coupled motion without relying solely on first-principles formulations. Building on this model, a non-singular predefined-time prescribed performance control (PT-PPC) strategy will be designed to ensure precise and robust attitude and motion control within guaranteed convergence time and performance bounds, while avoiding singularities. The control framework will be validated through simulations and experimental implementation on a free-floating robotic satellite testbed, demonstrating real-time feasibility and performance under realistic conditions. This project provides hands-on experience in advanced modelling, learning-based methods, and nonlinear control, with applications in on-orbit servicing, assembly, and space robotics systems.
Requirement to be on campus: Yes *dependent on government’s health advice.
Last updated 24 March 2026
