Has excellent research, analytical and problem-solving skills. Detail-oriented and able to learn new concepts quickly.
Overview
10
10
years of professional experience
Work History
Acoustic Engineer at
Stantec
Sydney, NSW
11.2023 - 05.2024
Noise modelling for infrastructure projects.
Site inspections in Sydney and wider NSW.
Acoustic data processing and analysis.
Technical report writing.
Building Sciences Engineer at
Mott McDonald
Adelaide , SA
07.2021 - 10.2023
Building Sciences Engineer in the Acoustics and Vibration Component of the AU Sciences Business. This role includes the following:
Spectral and temporal analyses of data to identify and quantify a variety of physical phenomena, such as acoustic signatures produced by vehicles passing over rumble-strips, and ground vibrations generated by the passage of trains.
Automated processing of data from sensors using Python and Excel.
Automated generation of Word reports that present large amounts of data in plots and tables, using Python.
Mathematical and statistical calculations on datasets using Excel and Python.
Technical report writing to present the results of modelling predictions and data, in accordance with client specifications.
Engineer Intern at
Arup
Sydney, NSW
11.2020 - 02.2021
I undertook a summer internship in the wind engineering group at Arup's Sydney office.
I worked on developing programs for processing climate data using Python and the data manipulation and analysis library Pandas.
This project consisted of updating an AWS Lambda API, and developing a Python client program to join and clean data sets according to user inputs.
Research Engineer at
German Aerospace Centre (DLR)
04.2015 - 02.2018
I undertook a master by research project at the German Aerospace Centre (DLR) titled 'Parametric System Identification of Rocket Combustion Instability using the Fokker-Planck Equation'
Scope: This work consisted of advanced analyses of experimental rocket combustor pressure data, which is noisy and challenging to process. These analyses used complex statistical signal processing techniques, based on a mathematical model of a randomly driven system. The results of the analyses were used to derive useful insights into rocket combustion instability.
Background: Combustion instability in a rocket engine leads to high pressure oscillations and subsequent structural damage to an engine, causing mid-flight mission loss. Since the mechanisms involved are not fully understood, rocket engine stability cannot be assured at the design stage. Consequently, costly ground testing of rocket engines is undertaken to ensure their stability. This project sought to advance understanding of this important problem in the space industry.
Where: the work was carried out at the DLR Institute of Space Propulsion in Lampoldshausen, Germany. The work was undertaken in collaboration with the Technical University of Munich (TUM) and the Swiss Federal Institute of Technology (ETH). This collaboration included visits and team work at TUM Munich and ETH Zurich.
Engineer Intern at
Bombardier Transportation
12.2013 - 02.2014
I undertook three months of paid engineering work experience at the Dry Creek Railcar Depot where Bombardier maintains Adelaide's train fleet.
I was one of only two engineers on site and thus learned how to communicate with non-technical stakeholders.
Education
Graduate Diploma - Machine Learning
University of Adelaide
12.2021
Graduate Diploma - Computer Science
University of Adelaide
12.2020
International Coding Bootcamp -
Le Wagon
11.2018
Master of Philosophy - Mechanical Engineering
University of Adelaide
07.2018
Bachelor of Engineering - Mechanical Engineering
University of Adelaide
12.2014
Skills
Signal processing: frequency-domain analyses and statistical analyses
Research skills: conducting a literature review and understanding scientific journals
Mathematical modelling of complex processes
Machine Learning and Big Data: use of Python scikit-learn machine learning library and Hadoop
Excel: implementation of calculations and data analyses
Ruby on Rails web development framework: basic competencies (Ruby, relational SQL databases, HTML, CSS, JavaScript, and Git competencies to develop software in a team on Github)
Linux OS: manipulation of folders and files, running jobs on computer clusters with PBS queues, and bash scripting
Python: intermediate competencies to manipulate and analyse data using Numpy and Pandas, and present data visualisations
C: intermediate competencies, used it in multiple third-year-level computer science assignments
Java: intermediate competencies, used it in a third-year-level computer science subject ‘Distributed Systems’ which involved socket programming and multi-threading
Matlab and Octave: advanced competencies in statistical signal processing, numerical solvers, and matrix manipulation