Postdoctoral Fellow​/Senior Research Fellow in Robust Vision Language Models

This Job is based in Australia

Postdoctoral Fellow / Senior Research Fellow in Robust Vision Language Models

Job no: 541415

Work type: Full Time

Location: Sydney, NSW

Categories: Post Doctoral Research Associate, Senior Research Associate

The School of Computer Science and Engineering has a fixed‑term opportunity for a Postdoctoral Fellow / Senior Research Fellow to contribute to world‑leading research in continual learning, computer vision, multimodal foundation models, and adversarial learning. Working on an exciting research project focused on developing continual and robust VLMs, you will investigate novel approaches to continual learning, model pre‑training and post‑training, with applications in multimodal understanding and reasoning.

This is an outstanding opportunity to undertake high‑impact research, collaborate with leading researchers and industry partners, and contribute to advancing the next generation of artificial intelligence technologies.

This position offers the opportunity to advance your scholarly research and professional activities on both national and international levels. You will contribute to the writing of scientific papers and reports for international journals, participate in conferences and workshops, supervise HDR students, and actively collaborate with industry partners.

This role reports to Professor Flora Salim and has no direct reports.

• Postdoctoral Fellow (Level
  
  B) – AUD $133,065 to $156,864 per annum + 17% superannuation
• Senior Research Fellow (Level C, Step
  1) – AUD $ 161,619 $ per annum + 17% superannuation
• Full time
• Fixed‑term contract – ASAP start until March 2029
• Location:
  
  Kensington – Sydney, Australia
• Australian citizenship required for project eligibility

UNSW is a world‑leading institution recognised for its scale, prestige, and impact. With strong industry engagement and partnerships across sectors, UNSW provides a unique environment where academic expertise translates into real‑world outcomes. The university is home to cutting‑edge research that drives innovation and societal progress, while its excellence in teaching ensures students are prepared to lead in their fields. For academics, UNSW offers an outstanding platform to flourish — combining world‑class facilities, collaborative networks, and a culture of innovation that supports both career growth and meaningful contributions to the wider community.

The School of Computer Science and Engineering is one of the largest and most prestigious schools of computing in Australia. It offers undergraduate programmes in Software Engineering, Computer Engineering, Computer Science, and Bioinformatics, as well as a number of combined degrees with other disciplines. Our research and teaching staff are world leading and world building as they advance knowledge and learning.

For more information on our school go to the following link - https://(Use the "Apply for this Job" box below).-engineering

• A PhD in Computer Science or a related discipline, and/or relevant work experience.
• Proven commitment to proactively keeping up to date with discipline knowledge and developments.
• Demonstrated track record in research, evidenced by publications in top‑tier AI and CV conferences and journals, such as CVPR, ICCV, ECCV, NeurIPS, ICLR, ICML, TPAMI, TNNLS, with outcomes of high quality and high impact with clear evidence of the desire and ability to continually achieve research excellence as well as the capacity for research leadership.
• Strong experience in the following areas:
  • deep learning and/or computer vision
  • continual learning
  • vision language models
• Experience in developing comprehensive benchmarks.
• Robust coding and software engineering skills.
• Experience in handling multimodal data, especially videos, is required.
• Experience with post‑tuning and/or model editing of multimodal LLMs is preferred.
• A track record of significant involvement with the profession and/or industry.
• High level communication skills and ability to network effectively and interact with a diverse range of students and staff.
• Demonstrated ability to work in a team, collaborate across disciplines…