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Role Description
This is a full-time, on-site role for a PhD Student at Deakin University, based in either Deakin's Waurn Ponds (Geelong) or Burwood (Melbourne) campus. The PhD Student will develop computational methods for modelling the impact response of spacecraft shielding. The student will employ adaptive experimental multi-objective optimisation using Bayesian optimisation - a probabilistic machine learning framework grounded in Gaussian process modelling and sequential decision theory. Additionally, statistical methods will be developed to allow the aleatory uncertainty in hypervelocity impact events to be quantified for next-generation risk assessment codes. Compared to current state-of-the-art methods, this framework enables statistically robust risk estimation and data-efficient exploration of high-dimensional design spaces. Data generation will be performed using high-fidelity explicit finite element simulations on the A2I2 high-performance computing cluster.
Qualifications
* A Bachelor\'s or Master\'s (preferred) degree in Mechanical Engineering, Aerospace Engineering, Electrical Engineering, Mechatronics, Physics, or Computer Science
* Proficiency in finite element theory and application, including modelling the dynamic behaviour of materials
* Programming experience in Python (preferred), Julia, or R
* Proficiency in statistics, optimisation, and linear algebra
* Familiarity with advanced research methodologies and related software tools.
* Demonstrated ability to work independently and take initiative in pursuing research objectives.
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