Western University: Ivey

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. The primary responsibility of this role is to deliver on an industry innovation research project where you will be part of the research team to develop a demonstration scale low pressure membrane system in collaboration with Atera Water Pte Ltd. Key Responsibilities Applicants are invited for a Research Engineer in low pressure membrane system (LPMS). Reporting to the Project Principal Investigator (PI), you will assist in all administration matters including overseeing, planning, forecasting, budgeting as well as ensuring the smooth running of the daily operations for the project Assists in co-supervision of final year projects (FYP) or capstone projects students or student helpers under the Project PI. Assist in designing, planning and setting up of a demonstration scale LPMS plant at a site allocated by PUB. Assist in designing, planning and setting up of a lab-scale LPMS plant at a SIT Punggol campus Assist in carrying out of experiment, data logging and analysis of data collected. Work with computational fluid dynamics (CFD) results generated by an expert to correlate with experimental data Assists the PI in drafting of reports, conference proceedings and journal articles based on the outcome of the projects. Prepares and shares fortnightly report of results from the computations with the program director and PI. Support the procurement and maintenance of the software/hardware under the charge of the PI Any other adhoc duties assigned. Requirements The appointee should have a strong foundation in fluid dynamics and understanding of fluid (air and water) flow is important. Appointee should also have experience with design of experiments, carrying out experiments and results analysis. Prior experience working with membrane systems will be a plus. A recognised degree from a good University B.Eng or M.Eng in Mechanical Engineering, Aeronautical Engineering, chemical or environmental engineering Strong foundation in Fluid Dyanmics Able to work independently with strong data analytical skills, communication, and interpersonal skills. Proficient in handling large data sets and the ability to analysis and interpret results Experience with internet of things setup and controller design (PLC or embedded computing) Key Competencies Good self-discipline and motivated to deliver Show strong initiative and take ownership of work 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

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