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Methodology
Last updated on 05/03/2026.
Department Of Primary Industries And Regional Development - Nsw
Machine Learning
Geological Survey of NSW AI/ML Proof of Concept application development - GSNSW seeks a vendor to rapidly build a cloud AI/ML PoC aligned with NSW AI Framework using Microsoft Fabric, ESRI ArcGIS, transformed GSNSW public data and skilled data science/engineering resources
Tender Close Date:
Department Of Primary Industries And Regional Development - Nsw
Machine Learning
Geological Survey of NSW AI/ML Proof of Concept application development - GSNSW seeks a vendor to rapidly build a cloud AI/ML PoC aligned with NSW AI Framework using Microsoft Fabric, ESRI ArcGIS, transformed GSNSW public data and skilled data science/engineering resources
Tender Close Date:
Department Of Defence - Dsrg
Machine Learning
Optical Terminal Guidance for Moving Targets - Develop, implement and demonstrate optical terminal guidance using small uncrewed aerial systems. - This procurement seeks to acquire and demonstrate optical terminal guidance for small uncrewed aerial systems to accurately detect, track, and engage moving targets without relying on GPS or external communications. This involves integrating several core technologies: machine learning for object detection and classification, object tracking across video frames, st
optical terminal guidance for moving targets
Tender Close Date:
Geoscience Australia
Machine Learning
Contract for Machine Learning- based Predictive Mapping - Machine learning- based predictive mapping of rare earth oxide ( REO) concentrations in surface rock and regolith across A- As part of the Australian Critical Minerals Research and Development Hub, Geoscience Australia is undertaking the Accelerating Development of Australia s Rare Earth Resources ( ADARER) project. The project aims to accelerate the discovery, extraction and processing of rare earth element ( REE) deposits, including clay- hosted
machine learningbased predictive mapping of rare earth oxide reo concentrations in surface rock and regolith across australia machine learningbased predictive mapping of rare earth oxide reo concentrations in surface rock and regolith across australia. development of continentwide predictive grids of rare earth oxide reo concentrations in surface rock and regolith, leveraging machine learning ml and geostatistical modelling approaches. part a: regionalscale proof of concept, utilising, for example, data from the northern australia geochemical survey nags, and other appropriate covariates. part b: nationalscale predictive grids of reo concentrations in surface rock and regolith across australia utilising, for example, data from the national geochemical survey of australia ngsa, and other appropriate covariates.
Tender Close Date:
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