Set up of Data Analytics for Riverland Councils IOT and Business Insights
Engineering, IT, Mathematics and Statistics
Please note; this internship is able to cover project costs for domestic students only.
Through the Murray Darling Basin Economic Development Program, the District Council of Loxton Waikerie is embarking on an exciting new IoT Smart-Agriculture Project. The first stage of the project involves the roll out of LoRaWAN IoT network across the region, that will then be utilised by council as well as being made available to the community and industry through The Things Network.
The project will deliver new use case projects across the broader Riverland region which will use IoT to address genuine community information needs, such as soil moisture monitoring, weather information, asset tracking, facility usage monitoring.
Through the delivery of the project, the region will be upskilled in the potential of IoT technology which will increase adoption across the highly developed agricultural sector in the area. The project also involves community led initiatives, and IoT technology for local high schools.
Data being captured will require varied outputs, including dashboards, alerts, integration into various existing systems, visualised web publishing, raw data sharing through Open Data. At this stage, Council’s preference is to use the PowerBi platform for business analytics.
RESEARCH TO BE CONDUCTED
It is expected that there will be a large volume of new data collected on various aspects of council operations and community projects that require a data-driven approach using both structured and unstructured data analytics to address real-world opportunities.
The Riverland Councils have only just begun their journey to improve their management insights and requires advice on the establishment of data solutions which will be sustainable for the region to manage and maintain on an ongoing basis.
Part of this internship will involve identifying opportunities to utilise datasets from both the IoT network and within councils systems, establishing data models, building data-driven taxonomies, automated data segmentation, identify anomalies and convert them into actionable insights to support council decision making and to provide benefits to the community.
Council is seeking advice and implementation of multiple data sources into various outputs.
SKILLS AND QUALIFICATIONS REQUIRED
We are looking for a PhD student with the following:
- Experience with data discovery, data cleansing, data modelling and making data structured.
- Experience and/or interest working with diverse data sets which could range from IOT sensor data to corporate transactional data
- Self-driven and ability to work autonomously with limited local technical expertise
- Strong written & verbal communication
- Dashboarding (PowerBI)
- Data Analytics
- Problem Solver
- Knowledge of IOT and Smart City technology and sensors
- Knowledge of GIS systems and geospatial data
- Computer Vision, Machine Learning or Natural Language Processing Experience
- Knowledge of programming languages (e.g. Python, R)
Establishment of data storage, data models, dashboards and visualisations for the LoRaWAN smart agriculture project and integration with councils existing systems (e.g. GIS and websites).
There will be an opportunity for the intern and council to discuss specific project outcomes prior to the internship commencing. As the project is expected to evolve as intern is placed, there may be an opportunity for the intern to recommend to council specific project outcomes 3 months into the placement.
The intern would be required to provide advice to councils about ongoing data requirements and solution design to ensure long term sustainability of data related business processes.
The intern will receive $18,000 over the duration of the internship, usually in the form of stipend payments.
It is expected that the intern will primarily undertake this research project during regular business hours, spending at least 80% of their time on-site with the industry partner. The intern will be expected to maintain contact with their academic mentor throughout the internship either through face-to-face or phone meetings as appropriate.
The intern and their academic mentor will have the opportunity to negotiate the project’s scope, milestones and timeline during the project planning stage.
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