Research Associate – last-mile logistics

About the role

The appointee will work closely with researchers and industry practitioners from UNSW and Adiona Tech, as well as other industry partners, to develop and validate AI-powered methods for sustainable freight and last-mile logistics as part of a CRC-P project. The research will focus on optimising last-mile delivery operations, reducing emissions, and accelerating fleet electrification. A key objective is to translate an existing research prototype into a commercial-scale platform capable of supporting real-world logistics networks across Australia.

The position provides a distinctive opportunity to undertake high-quality academic research while working directly with industry datasets, operational systems, and commercial software development. This is a unique industry postdoctoral position comprising a 0.8 FTE appointment through UNSW and a complementary 0.2 FTE appointment through Adiona Tech.

Salary

Level A – $95,036 – $126,711 per annum + 17% superannuation (pro rata)

Schedule

Part time: 28 hours (0.8 FTE) and 0.2 FTE appointment through Adiona Tech. Fixed-term contract: 12 months (potential for extension). Location: Kensington – Sydney, Australia (on campus attendance 3 days a week).

About UNSW

UNSW is a large organisation with a diverse and talented community, driven to be thoughtful, practical, and purposeful. The Faculty of Engineering at UNSW is a national and international leader in engineering research and education, graduating the highest number of engineers in Australia and maintaining the largest research expenditure in the country. The Faculty strives to sustain and further its global impact, aiming to be ranked among the top engineering faculties worldwide.

Skills & Experience

• A PhD in a related discipline, and/or relevant work experience. Relevant disciplines may include transport engineering, computer science, artificial intelligence, data science, machine learning, operations research, mathematics, statistics, or another quantitative discipline.
• Proven commitment to proactively keeping up to date with discipline knowledge and developments.
• Demonstrated ability to undertake high-quality academic research and conduct independent research with limited supervision.
• Demonstrated track record of publications and conference presentations relative to opportunity.
• Demonstrated expertise in one or more of the following areas: mathematical optimisation, vehicle routing and scheduling, machine learning, reinforcement learning, logistics simulation, predictive analytics, or transport modelling.
• Strong programming skills in Python and experience developing, implementing, and testing computational models or research software.
• Experience working with large and complex datasets and applying appropriate data management, analysis, and validation methods.
• Demonstrated ability to translate research concepts and computational methods into practical solutions suitable for real-world implementation.
• Experience with high-performance computing, cloud computing, software development, or commercial technology platforms would be highly regarded.
• Knowledge of electric vehicle operations, energy and charging models, emissions estimation, freight transport, or logistics decarbonisation would be desirable.
• Demonstrated ability to work in a team, collaborate across disciplines and build effective relationships.
• Demonstrated ability to collaborate effectively with industry partners and communicate technical concepts to both specialist and non-specialist audiences.
• Evidence of highly developed interpersonal skills.
• Demonstrated ability to communicate and interact with a diverse range of stakeholders and students.
• Ability to work effectively across university and industry environments and manage competing research, technical, and project priorities.