The Australian Academy of Science’s Science Policy internship program has two internship opportunities, each for a period of three months.

The goal of this project is to create a quality management system for a distributed clinical trial network across Australia based on lean management principles, technological approach to manage and distribute processes and work instructions and in addition, an ongoing approach to improvement using the Plan, Do, Check, Act cycle.

To build robust data pipelines and support the development of novel AI techniques for multi-class and multi-label classification of embryos with application to genetic (PGT-A) screening for improved IVF outcomes.

To accurately measure stock levels in silos, in a production and farm context, to improve procurement and supply chain logistics in the agri-feed sector.

Research and development of heavy duty floor coatings for the food and beverage industry, with a particular focus on properties to improve effectiveness in combating bacteria and foodborne pathogens, including their growth and transmission. This involves assessing and incorporating anti-microbial additives, along with other improvements such as lowering dirt retention, improving cleanability, etc.

Plastic is a ubiquitous part of modern life, but it has the potential to cause harm to the environment and human health if not effectively regulated. The Environment Protection Authority (EPA) Victoria wants to address key knowledge gaps relating to high-use plastics and microplastics in circulation within Victoria. We are looking for an enthusiastic student to help EPA’s applied scientists identify and evaluate potential mitigation measures and controls for key sources of plastics and microplastics in Victoria.

Replacing mass production of carpet with Internet enabled carpet design and personalisation, to increase carpet industry sustainability through a disruptive business model.

During the internship, students will work with IoT data LiXiA PUKs (propriety suite of sensors) to develop and deploy novel machine learning algorithms. The students will work on algorithms for prediction and anomaly detection to help clients to make data driven decisions for maintenance of utilities infrastructure.

Harnessing IR4.0 manufacturing to increase carpet industry sustainability.