PhD Intern - Automated process mapping ML model
Please note you must be a 2nd or 3rd year PhD student to apply.
Adiona develops advanced logistics optimisation algorithms and software. Funded by the Australian Research Council and partnered with UNSW, our technology helps mobile fleets achieve efficient, minimal cost operation and is a practical solution for improving the sustainability of our cities.
Process Mapping is a technique commonly used by supply chain and logistics professionals and consultants. Using both qualitative and quantitative methods, it requires a high level of expertise, knowledge of the system, and labour to perform. A google search will turn up numerous examples.
The goal of this project is to create a machine learning model that can automate parts of this process given a set of known data types with a known schema but varying network flow structures. The model will first need to map the relationships between data types, then construct the process map by tracing the journey of data in linear time. Adiona will provide a variety of real customer data sets that can be used to create and train the model.
If you’re a PhD student and meet some or all the below we want to hear from you. We strongly encourage women, indigenous and disadvantaged candidates to apply:
- Working with large data sets.
- Machine learning, NLP, feature learning or other relevant knowledge that can solve the problem.
- Relevant tools such as Tensorflow, R, Matlab, Visio, or others.
- Team spirit and can-do attitude are a must!
- Ability to work closely with a team and also independently.