Research Associate/Senior Research Associate in Multimodal Foundation Models

Research Associate/Senior Research Associate in Multimodal Foundation Models Join to apply for the Research Associate/Senior Research Associate in Multimodal Foundation Models role at UNSW. The Opportunity The School of Computer Science and Engineering is offering an opportunity for a Research Associate/Senior Research Associate. In this role, you will conduct both independent and collaborative research within your appointed field and undertake activities to further develop your expertise in deep learning. You will be working on a defence funded project with the research focused on the development of multimodal foundation models using data from a broad range of modalities. The project will develop techniques for pre‑training (such as continual learning and lifelong learning) and post‑training (for example, instruction fine‑tuning and task‑agnostic prompting) that are particularly adapted for robustness across new modalities and tasks, and against adversarial attacks. The models will be evaluated on downstream tasks related to situational understanding and contextual intelligence. 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. Salary: Research Associate (Level A) – AUD$113,911 to $121,838 per annum + 17% superannuation Senior Research Associate (Level B, Step 1) – AUD$127,947 per annum + 17% superannuation Full time Fixed‑term contract – 3.5 years starting in January 2026 Location: Kensington – Sydney, Australia Due to project timelines to be considered for this role, candidates must have working rights for Australia Skills and Experience Research Associate (Level A) A PhD (or soon to be awarded) in a related discipline, and/or relevant work experience. Demonstrated track record in research, evidenced by publications in top‑tier AI and ML conferences (e.g. CVPR, ICCV, ECCV, NeurIPS, ICLR, ICML), with outcomes of high quality and high impact, and a clear evidence of the desire and ability to continually achieve research excellence and the capacity for research leadership. Strong experience in at least two of 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. A strong practice of maintaining open‑source code repository. Demonstrated ability to work in a team, collaborate across disciplines and build effective relationships. Evidence of highly developed interpersonal skills. An understanding of and commitment to UNSW’s aims, objectives and values in action, together with relevant policies and guidelines. Knowledge of health and safety responsibilities and commitment to attending relevant health and safety training. Senior Research Associate (Level B) A PhD in a related discipline, and/or relevant work experience. Demonstrated track record in research, evidenced by publications in top‑tier AI and ML conferences (e.g. CVPR, ICCV, ECCV, NeurIPS, ICLR, ICML), with outcomes of high quality and high impact, and a clear evidence of the desire and ability to continually achieve research excellence and the capacity for research leadership. Strong experience in all of 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. Proven commitment to proactively keeping up to date with discipline knowledge and developments. A track record of significant involvement with the profession and/or industry. A strong practice of maintaining open‑source code repository. 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 and build effective relationships. Evidence of highly developed interpersonal skills. Ability to build effective networks with colleagues. Ability to generate alternative funding and/or industry projects through effective liaison with external local and international researchers, industry and government. An understanding of and commitment to UNSW’s aims, objectives and values in action, together with relevant policies and guidelines. Knowledge of health and safety responsibilities and commitment to attending relevant health and safety training. Additional details about specific responsibilities for this position can be found in the position description. This is available via JOBS@UNSW. To Apply Please click the 'Apply now' button and submit your CV, Cover Letter and Responses to the Skills and Experience. You should systematically address the Skills and Experience listed within the position description in your application. Applicants must have working rights in Australia and be able to be on site in Kensington. Visa sponsorship is not available for this appointment. Please note applications will not be accepted if sent to the contact listed below. Contact Eugene Aves – Talent Acquisition Consultant E: eugene.aves@unsw.edu.au Applications close: 11:55 pm (Sydney time) on Sunday 14 December 2025 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. #J-18808-Ljbffr