Internship Opportunity

Reference no: 2022.56T

Location: Fitzroy North, VIC

Engineering areas: Data Science, Data Analytics, Machine Learning and Artificial Intelligence

Business Description

We develop model-based engineering software that provides the integrated analyses required to conduct engineering risk management and optimization. We are developing Syndrome Diagnostics (SD), a causation-based AI software for real-time Fault Detection and Isolation in complex mission/safety-critical systems. The software leverages a digital risk twin and uses machine learning algorithms to detect incipient failures for system health assessment – an integral component of Predictive Maintenance processes.

Projects / tasks

Development of the Syndrome Diagnostics (and other) software solution(s) to monitor / analyze operational data required for system health assessment for sustainment (maintenance) optimization. The work will involve the use of different machine learning algorithms, such as SVM, Neural Networks, Clustering and their optimisation. It will also include some analysis and visualisation techniques directly contributing in the development of the SD product.

Duties will encompass development of a software solution to monitor / analyse operational data required for system health assessment for sustainment (maintenance) optimization by utilizing data science/machine learning skills and knowledge to detect, isolate, and visualize failures within a system using sensor signals.

Depending on the rate of progress, the intern may work on any of the following and more:

1. Time series prediction and forecasting
2. Trend extraction
3. Real sensor signal data handling
4. Pattern analysis
5. Failure Classifications
6. Automatic failure detection
7. Deep neural network teaching
8. Statistical modelling
9. Training and optimisation methods
10. Software and method testing

Technical skills

Strong mathematical background, experience in any programming language (ideally Python), exposure to Machine Learning and/or Data Science. Added exposure to Amesim, SysML well regarded but not required.

Intern interpersonal qualities

Communication skills are important, rapid knowledge acquisition / application capability, preferably penultimate or final year students that could transition to FTE or PTE upon completion of internship.

Bright, personable candidates eager to prove themselves and apply theoretical knowledge to real data-science and engineering applications. Candidates who are involved in extra-curricular activities (data-science/engineering-related or otherwise [past interns were involved in Robogals, Formula SAE, local sports, etc.]) well regarded.

Candidates with broad and varying backgrounds / work experience (different cultural backgrounds, worked in different non-engineering industries [i.e., hospitality, retail, finance, etc.], lived in different cities/countries).