Research Associate - Last-Mile Logistics

University Of New South Wales; Sydney; On-site; AUD 95,000 - 127,000; Full time

Job summary

University of New South Wales invites applications for a Research Associate in last-mile logistics. The role involves developing AI-powered methods to optimise delivery operations, reduce emissions, and accelerate fleet electrification within a CRC-P project.

Work closely with UNSW researchers, Adiona Tech, and industry partners on real datasets and commercial software development. The appointment is 0.8FTE through UNSW with a complementary 0.2FTE through Adiona Tech, for 12 months with

Qualifications

• PhD or relevant work experience in a quantitative field.
• Publications and conference presentations are valued.
• Ability to translate research concepts into practical solutions.

Responsibilities

• Develop and validate AI-powered methods for sustainable logistics.
• Collaborate with UNSW, Adiona Tech, and industry partners on data-driven research.
• Translate prototype research into a commercial-scale platform for Australian networks.

Skills

Python programming

Data analysis

Collaboration

Research communication

Education

PhD in related discipline

Tools

Python

Computational models

Job description

Research Associate - last-mile logistics University of New South Wales•Sydney, NSW, AU

Job Description

Research Associate – last-mile logistics

About University of New South Wales

At UNSW, we take pride in the broad range and high quality of our teaching programs. Our teaching gains strength and currency from our research activities, strong industry links and our international nature; UNSW has strong regional and...

The Opportunity: 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 0.8FTE 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)
• Part Time: 28 hours (0.8FTE) 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 isn't like other places you've worked. Yes, we're a large organisation with a diverse and talented community; a community doing extraordinary things. But what makes us different isn't only what we do, it's how we do it. Together, we are driven to be thoughtful, practical, and purposeful in all we do. If you want a career where you can thrive, be challenged and do meaningful work, you're in the right place.

The Faculty of Engineering at UNSW continues to be a national and international leader in engineering research and education, cementing its status as the University's premier research faculty. As the largest Engineering Faculty in Australia, it graduates the highest number of engineers and maintains the largest research expenditure in the country. Through excellence in both fundamental and applied research, the Faculty strives to sustain and further its global impact, aiming to be ranked among the top engineering faculties worldwide. For more information, visit the Faculty website: https://www.unsw.edu.au/engineering/about-us

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.

Pre-Employment Checks: As part of our recruitment process candidates may be required to undergo pre-employment screening, which may include reference checks, qualification verification, right-to-work verification, and criminal history screening where relevant to the role.

Compliance with the necessary combination of these checks is a condition of employment at UNSW. Additional details about the specific responsibilities for these positions can be found in the position description. This is available via JOBS@UNSW.

Applications close: 11:55 pm (Sydney time) on Tuesday 22 nd September 2026

UNSW is committed to evolving a culture that embraces equity and supports a diverse and inclusive community where everyone can participate fairly, in a safe and respectful environment. We welcome candidates from all backgrounds and encourage applications from people of diverse gender, sexual orientation, cultural and linguistic backgrounds, Aboriginal and Torres Strait Islander background, people with disability and those with caring and family responsibilities. UNSW provides workplace adjustments for people with disability, and access to flexible work options for eligible staff. The University reserves the right not to proceed with any appointment.