Imperial School of Business and Science

As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will have the opportunity to be equipped with applied research skill sets that are relevant to industry demands while working on research projects in SIT. This project focuses on federated causal inference in heterogeneous data environments, addressing the challenge of enabling trustworthy causal analysis across distributed datasets while preserving privacy. The successful candidate will be responsible for the end-to-end investigation of novel federated learning strategies for causal inference. The role will bridge rigorous theoretical work with hands-on algorithm design and development on real-world datasets. The core responsibility is to build and validate federated causal inference algorithms through simulations and live demonstrations. Key Responsibilities Participate in and manage the research project with Principal Investigator (PI) to ensure all project deliverables are met. Derivation of novel performance metrics for federated causal inference algorithms. Analysis of causal inference models in federated settings using synthetic and real-world datasets. Design and development of novel federated causal inference algorithms and associated software APIs. Validation of algorithms via simulations and live demonstrations. Job Requirements A Master's degree or higher in Computer Engineering, Computer Science, Data Science, Statistics, or equivalent. Strong theoretical background in statistics and machine learning. Knowledge of the basics of federated learning and causal inference is highly encouraged. Proven track record in research and development of machine learning algorithms. Proficiency in algorithm development using Python and ML frameworks such as PyTorch or TensorFlow. Key Competencies Work independently, as well as within a team, to ensure proper operation and maintenance of equipment. Able to build and maintain strong working relationships with people within and external to the university. Self-directed learner who believes in continuous learning and development. Proficient in technical writing and presentation. Possess strong analytical and critical thinking skills.

Role type: Full time, Fixed Term (3 years) Faculty: Faculty of Science School: School of Mathematics and Statistics Salary: Level A: $85,555 - $116,094 p.a. plus 17% super Level B: $122,212 - $145,121 p.a. plus 17% super ARC funded stochastic optimisation project on correlated restless bandits / collaborate with leading international investigators / develop independent research profile Join us to shape new theory and applications in stochastic optimisation and mentor emerging researchers. Investing in you - 17% superannuation, benefits package including salary packaging, health and wellbeing programs, discounted services, and professional development opportunities. The University of Melbourne We take pride in our people, who all contribute to our mission to benefit society through the transformative impact of education and research. Discover more via our website. Your next career opportunity You will join an ARC Discovery team advancing stochastic optimisation for highly correlated restless bandit models. You will collaborate with leading investigators across Melbourne, RMIT and Spain, developing your independent research profile. You will also co-supervise research students and contribute to teaching within a world-class mathematics school. What you will deliver: Conduct high quality research in stochastic optimisation, focusing on theory and applications of correlated restless bandit models. Collaborate closely with chief investigators and research partners to design studies, analyse results and publish impactful outputs. Prepare and contribute to competitive grant proposals and related research documentation under guidance from senior academics. Co-supervise and mentor postgraduate and honours students, supporting their research training and academic development. Present research at seminars and conferences, and participate in School service, outreach and diversity activities. You may be a great fit if you: Hold or submit a PhD in mathematics, optimisation, stochastic modelling or a closely related quantitative discipline. Demonstrate strong expertise in stochastic optimisation, including experience applying advanced methods to complex modelling problems. Bring experience contributing to research projects, from problem formulation to analysis, publication and dissemination of findings. Contribute to supervision or mentoring of undergraduate or postgraduate research students in mathematical or related fields. Work effectively within collaborative research teams while also progressing individual projects with limited supervision. Communicate research clearly in English, both in writing and presentations, to academic and broader audiences. For further information please refer to the attached PD. What we offer you! We offer the opportunity to be part of a vibrant community and enjoy a comprehensive range of benefits to support your success and sense of fulfilment, including: Build future financial security with 17% superannuation Supportive flexible work arrangements underpinned by our commitment to inclusion and well-being Career development opportunities, including access to a range of tailored programs, such as Academic Women in Leadership, and learning platforms including LinkedIn Learning Progressive, considerate leave provisions to empower your work-life balance and well-being, including leading parental leave, gender affirmation leave and cultural leave Salary packaging and access to a range of discounted services including Bupa health insurance, and access to unique cultural and recreational benefits such as discounts for the Melbourne Theatre Company Health and well-being services including a leading Employee Assistance Program For more information check out our benefits page! Your new team - School of Mathematics and Statistics Joining the School of Mathematics and Statistics, you will contribute within one of Australia's leading mathematical sciences communities. The school hosts numerous ARC-funded centres and fellows, with outstanding infrastructure and strong international research collaborations. Within the Faculty of Science, you will join a diverse, supportive environment that values curiosity, excellence and inclusion. Be Yourself The University of Melbourne is an Equal Opportunity Employer and a child-safe organisation. The diversity of our community enriches us all, and we are committed to creating an inclusive and fair workplace where everyone is valued, respected, and empowered to succeed. We welcome applicants from all backgrounds, identities, and experiences. Discover more about Diversity and Inclusion at UniMelb. We're committed to a barrier-free recruitment process and ongoing workplace support, providing adjustments throughout. We warmly encourage applications from people with disabilities. Learn more about how we support an accessible recruitment process. Aboriginal and Torres Strait Islander Applicants We aspire to be the University of choice for Indigenous Australians. Visit our Indigenous staff page to learn more about our investment and support for Aboriginal and Torres Strait Islander staff. Indigenous applicants are encouraged to connect with our Indigenous Employment & Development team at oied-hr@unimelb.edu.au. Application essentials: Visit "apply with us" to learn more about the process, including tips and FAQs. This role is eligible for visa sponsorship and we welcome international applicants. If successful, we will support you through this process. A Working with Children Check is required for all positions. If successful, we will guide you through the process and reimburse you. Please upload your resume and a cover letter outlining your interest and experience as part of the application process. Please upload your responses to the Selection Criteria, found in the Position Description. Want to know more? For queries related to this specific position or for questions related to our recruitment process email Lachlan Bryce at hr-careers@unimelb.edu.au For recruitment adjustments contact Kim Groizard on +61 3 9035 3218 or at hr-careers@unimelb.edu.au (subject: 'Recruitment Adjustments'). Apply today, and join a community that's shaping the future. Applications close: Saturday 11 July 11:55 PM; Melbourne time zone. Position Description: JR-013912 Postdoctoral Fellow in Stochastic Optimisation_PD.doc JR-013912 Postdoctoral Fellow in Stochastic Optimisation_PD.pdf

About RoboPrecinct@PDD SmartPrecinct@PDD initiative aims to develop a precinct-scale robotics and embodied AI ecosystem at Punggol Digital District (PDD). The programme establishes shared digital infrastructure, regulatory frameworks (including cybersecurity and safety), embodied AI capabilities, industry collaborations, and real-world test-bedding environments to enable safe and scaled deployment of robots in PDD. The Programme Manager will lead the Programme Office and support the strategic planning, development, coordination, and governance of the SmartPrecinct@PDD programme. Key Responsibilities Programme management Reporting to the Lead PI of the SmartPrecinct@PDD programme, the Senior Programme Manager will oversee the overall execution and progress monitoring across multiple work packages in the programme. Coordinate multi-disciplinary activities to ensure alignment of technical development, infrastructure deployment, and regulatory initiatives. Support the principal investigators (PIs) to conduct technical integration between work packages where applicable. Track programme milestones, deliverables, budget, and reporting to the grantor and institution leadership. Supervise other Programme Office staff and coordinate administrative support functions. Stakeholder and governance management Lead engagement with government agencies and other stakeholders to develop governance frameworks for robot operations in PDD. Coordinate development of policies and agreements related to robot certification, data sharing, safety compliance, and cybersecurity standards. Support programme steering committees by preparing briefings, reports, and strategic updates. Ecosystem development Identify strategic collaborations with new industry and public sector partners to grow the SmartPrecinct@PDD ecosystem. Support programme expansion initiatives and new funding opportunities. Requirements Minimum Bachelor’s degree in engineering, robotics, computer science, AI, cybersecurity, or a related field. Ideally 10+ years of relevant experience in programme management, technology development, or public-sector innovation initiatives, with strong technical background in related fields. Experience managing large multi-stakeholder projects involving government agencies, industry partners, and research institutions. Strong strategic thinking and stakeholder engagement capabilities. Proven ability to manage complex programmes involving multiple technical teams and deliverables. Excellent communication and leadership skills. *Note: The incumbent will be appointed as REsearch Engineer/Research Fellow if successful.