Saudi Electronic University

Part-time (17.5 hours per week), 12-months fixed-term opportunity Join a leading neuroimaging research team at the University of Sydney and contribute to groundbreaking multiple sclerosis research while developing your expertise in MRI analysis, data science, and clinical research coordination Base Salary $89,755 - $100,495 (pro-rata) + 17% superannuation About the opportunity Join the Save Sight Institute at the University of Sydney and play a key role in a major MRFF-funded multiple sclerosis neuroimaging study that is advancing the understanding of disease progression and treatment outcomes. Working within a multidisciplinary research team, you will contribute to the delivery of cutting-edge research through a combination of advanced MRI data processing, neuroimaging workflow coordination, and study administration. We are seeking a highly organised and technically capable Research Assistant to support a large MRFF-funded multiple sclerosis imaging study based at the University of Sydney. The role combines advanced neuroimaging data management and MRI pipeline execution with day-to-day study coordination responsibilities. The successful candidate will work with large-scale longitudinal clinical and imaging datasets derived from the MSBase Registry and MSBase Imaging Repository (MSBIR), including extraction and organisation of treatment and MRI timepoint data, coordination of imaging workflows, and execution of MRI preprocessing pipelines to prepare data for downstream lesion analysis. Responsibilities will include image registration, quality control, pipeline management, coordination of data flow across collaborating sites, and assisting with overall study administration and project coordination. The position requires experience working with MRI analysis pipelines and neuroimaging software (e.g. FSL, ANTs, FreeSurfer, MRtrix or similar), strong organisational skills, and the ability to work independently within a multidisciplinary MS neuroimaging research team. The role is expected to involve approximately 80% MRI/data processing work and 20% study coordination activities. Your key responsibilities will be to: Coordinate and support the delivery of a large-scale multiple sclerosis neuroimaging research study, ensuring research activities are completed accurately and within agreed timeframes. Manage and process complex longitudinal clinical and MRI datasets, including data extraction, organisation, quality control, and preparation for downstream analysis. Execute and maintain MRI preprocessing and analysis pipelines, supporting image registration, workflow management, and data integrity across collaborating research sites. Collaborate with researchers, clinicians, and external partners to facilitate study operations, coordinate imaging workflows, and contribute to project objectives and outcomes. Prepare and maintain study documentation, reports, ethics and governance records, research outputs, and other project-related materials in accordance with regulatory and institutional requirements. Support the administrative and operational requirements of the research program, including stakeholder communication, financial processing, compliance activities, and continuous improvement initiatives. About you Tertiary qualifications in a relevant field. Demonstrated experience working with MRI analysis pipelines and neuroimaging software such as FSL, ANTs, FreeSurfer, MRtrix, or similar tools, with the ability to manage and process complex imaging datasets. Strong data management, analytical and problem-solving skills, including experience handling large-scale longitudinal research data and maintaining high standards of data quality and accuracy. Excellent organisational, communication and stakeholder management skills, with the ability to coordinate multiple priorities and work effectively across multidisciplinary research teams and collaborating organisations. Experience working in a research-intensive environment, including supporting research administration, literature reviews, data reporting, and the preparation of research outputs and project documentation. A proactive and collaborative approach, with strong attention to detail, sound judgement, and the ability to work independently while contributing positively to team objectives. Sponsorship / work rights for Australia You must have unrestricted work rights in Australia for the duration of this employment to be eligible to apply. Visa sponsorship is not available for this appointment. Pre-employment checks and declarations Your employment is conditional upon the successful completion of all pre-employment or background checks required for the role in terms satisfactory to the University. Also, to meet the University's obligations under the National Higher Education Code to Prevent and Eliminate Gender-Based Violence you will be asked to declare if you have been investigated for, or found to engaged in, sexual harm or gender-based violence in the course of previous employment or in a legal process. Similarly, your ongoing employment is conditional upon the satisfactory maintenance of all relevant clearances and background check requirements. If you do not meet these conditions, the University may take any necessary step, including the termination of your employment. EEO statement At the University of Sydney, our shared values are trust, accountability and excellence and we strive to be a place where everyone can thrive. We are committed to creating a University community that thrives through diversity and reflects the wider community that we serve. We deliver on this through our commitment to diversity and inclusion, evidenced by our people and culture programs, as well as key strategies to increase participation and support the careers of Aboriginal and Torres Strait Islander People, women, people living with a disability, people from culturally and linguistically diverse backgrounds, and those who identify as LGBTQIA+. We welcome applications from candidates from all backgrounds. We are proud to be recognised as an Australian Workplace Equality Index (AWEI) Platinum Employer. Find out more about our work on diversity and inclusion. How to apply Applications (including a cover letter, CV, and any additional supporting documentation) can be submitted via the Apply button at the top of the page. For employees of the University or contingent workers, please login into your Workday account and navigate to the Career icon on your Dashboard. Click on USYD Find Jobs and apply. For a confidential discussion about the role, or if you require reasonable adjustment or any documents in alternate formats, please contact Roshik Prasad, Recruitment Operations by email to roshik.prasad@sydney.edu.au Copyright The University of Sydney The University reserves the right not to proceed with any appointment. Click to view the Position Description for this role. Applications Close Sunday 12 July 2026 11:59 PM

The Singapore Institute of Technology (SIT) is Singapore’s first University of Applied Learning, offering industry-relevant degree programmes that prepare its graduates to be work- and future-ready professionals. Its mission is to maximise the potential of its learners and to innovate with industry, through an integrated applied learning and research approach, so as to contribute to the economy and society. The university’s unique pedagogy integrates work and study, embracing authentic learning in a real-world environment through collaborations with key strategic partners. Its focus on applied research with business impact is aimed at helping industry innovate and grow. Targeted to be ready in 2024, SIT’s centralised campus within the larger Punggol Digital District will feature a vibrant learning environment where academia and industry will be tightly integrated with the community. SIT espouses an applied learning approach and offers degrees with a strong focus on science and technology. Our faculty work closely with industry on translational research and innovation, as well as provide consultancy on solutions to industry challenges. This well-established nexus with industry endows our graduates with deep skills and work-ready training experience – attuning them to the needs and demands of the future economy. SIT is expanding its Robotics and Embodied AI (EAI) capabilities, building a cohort of academic staff who are keen and experienced working on industry problems in relevant areas. At SIT, Robotics and Embodied AI faculty will have the opportunity to shape and lead applied research in a unique real-world environment. Located within Punggol Digital District (PDD), SIT’s purpose-built campus serves as a living lab for precinct-scale robotics test-bedding, where faculty can work with government and industry partners to develop, validate and deploy next-generation robotic systems safely and at scale. Candidates will contribute to industry-relevant research in embodied AI, simulation and digital twins, cybersecurity, robot operations and related areas, with a strong emphasis on translation into real-world applications. Through collaborations with leading robot companies and end-users, faculty will help solve practical deployment challenges, build shared digital infrastructure, and train the next generation of robotics talent for Singapore’s future economy. Responsibilities: Lead and contribute to applied research projects with industry partners and government agencies. Develop and teach Robotics and Embodied AI courses within the cluster’s programmes and across relevant interdisciplinary modules. Supervise Industry Doctorate/Masters and capstone projects in collaboration with domain faculty and industry partners. Contribute to SIT’s continuing education offerings. Serve as embodied AI mentor for existing SIT domain faculty through joint projects and co-supervision mechanisms. Qualifications: PhD in Robotics, Mechanical/Electronics Engineering, Computer Science, or a relevant domain with demonstrated embodied AI research experience. Equivalent industry experience may also be considered. Technical expertise and track record in the following areas (ideally at least two): Policy Learning and Robotics Control: Imitation / reinforcement learning, generative policies, residual learning, cross-embodiment generalisation. Robotics Data Engineering: Data capture, synchronisation, calibration, evaluation harnesses and simulation data pipelines Foundation Models & World Models for Robotics: VLA grounding, planning interfaces and world-model reasoning for robots State Estimation, Perception and Sensor Fusion: 3D scene understanding, diagnostics, active and manipulation-centric perception System Integration, Deployment & RobotOps: ROS2 systems integration, runtime robustness, safety and field deployment engineering Swarm Robotics & Multi-Agent Systems: Decentralised coordination, shared mapping and resilient communications Fleet Management & Remote Operations: Mission control, tele-operation, network resilience and lifecycle operations Cyber-Physical Security & Trustworthy Robotics: Threat modelling, resilience, incident response, AI ethics and data privacy Experience applying embodied AI to real-world use areas will be advantageous, such as (non-exhaustive): Facility Management Healthcare Advanced Manufacturing Urban Systems Biotechnology & Chemical Engineering Demonstrated leadership and evidence of working on industry/society-defined problems Strong connections with international partners and organisations will be advantageous. Experience with industry research collaborations, applied research grants, and/or system deployment Established thought leadership and/or professional standing e.g. publications in top journals and keynotes, in addition to non-publication outputs and impact such as open-source tools, technical reports, patents, deployed systems Demonstrated track record of cross-disciplinary collaborations Ability to communicate clearly with domain experts, industry practitioners and general public What SIT offers: An applied research environment, working together with industry partners, to tackle real-world problems SIT values applied research impact and evaluates faculty accordingly on how their research had made a difference to society or industry Opportunities to work on a coherent robotics & embodied AI research agenda with colleagues across disciplines Access to Punggol Digital District ecosystem as testbeds for applied research A meaningful role in building Singapore’s robotics & embodied AI research capability at a pivotal moment Singapore’s exceptional quality of life, competitive personal tax environment, and position as Asia’s leading research and innovation hub How to apply: Candidates should submit the following: Curriculum vitae including grants, outputs (code, datasets, patents, technical disclosures) and publications Cover Letter Research statement (up to 2 pages) addressing your applied AI research programme, how it connects to SIT’s cluster context, and your experience with industry-connected research Teaching statement (up to 1 page) addressing your approach to applied learning Three highlights of research outputs with a brief explanation of each output’s impact

Assistant Professor / Associate Professor / Professor in Aerospace MRO (Smart Maintenance & Repair Systems) The Singapore Institute of Technology (SIT) is Singapore’s first University of Applied Learning, offering industry-relevant degree programmes that prepare its graduates to be work- and future-ready professionals. Its mission is to maximise the potential of its learners and to innovate with industry, through an integrated applied learning and research approach, so as to contribute to the economy and society. The university’s unique pedagogy integrates work and study, embracing authentic learning in a real-world environment through collaborations with key strategic partners. Its focus on applied research with business impact is aimed at helping industry innovate and grow. SIT’s centralised campus within the Punggol Digital District features a vibrant learning environment where academia and industry are tightly integrated with the community. SIT espouses an applied learning approach and offers degrees with a strong focus on science and technology. Our faculty work closely with industry on translational research and innovation, as well as provide consultancy on solutions to industry challenges. This well-established nexus with industry endows our graduates with deep skills and work-ready training experience. SIT is strengthening its capabilities in Aerospace Maintenance, Repair and Overhaul (MRO), building a cohort of faculty who are experienced in solving real-world aerospace maintenance challenges through applied research and innovation. Focus areas for Aerospace MRO include: Smart Maintenance & Predictive Diagnostics Digital Twins & Lifecycle Engineering Robotics & Autonomous Inspection Systems Composite Structures Inspection & Repair Advanced Repair Technologies (e.g. Additive Manufacturing) Responsibilities: Lead and contribute to applied research projects with industry partners and government agencies in Aerospace MRO Develop and teach Aerospace MRO-related modules within SIT’s Aircraft Systems Engineering (ASE) and interdisciplinary programmes Supervise Industry Doctorate/Masters and capstone projects in collaboration with domain faculty and industry partners Contribute to SIT’s continuing education and workforce upskilling programmes (CET/CSM) Drive translational research outcomes, including deployment of solutions in operational MRO environments Serve as a technical mentor across disciplines (AI, robotics, materials), enabling cross-domain integration for smart MRO systems Qualifications: PhD in Aerospace Engineering, Mechanical Engineering or a closely related discipline Strong track record in Aerospace MRO, aerospace systems, or related applied research/industry practice Technical expertise and deep knowledge of one or more of the following areas: Predictive Maintenance & Prognostics (PHM) Structural Health Monitoring (SHM) and NDT/NDI Digital Twins and physics-informed modelling Aerospace robotics and autonomous inspection systems Composite materials, damage detection, and repair Additive manufacturing for aerospace repair (flight-certified applications) Aircraft systems, gas turbine maintenance, or MRO best practices Circular economy and sustainable aerospace maintenance Experience Applying Expertise to one or more of the Following: Commercial or military Aerospace MRO operations Smart hangar / digital MRO transformation initiatives AI-enabled inspection and diagnostics systems Robotics deployment in maintenance environments Lifecycle management of composite aircraft structures Industry-scale maintenance optimisation and downtime reduction Demonstrated leadership and evidence of working on industry/society-defined problems Experience with industry research collaborations, applied research grants, and/or system deployment Established thought leadership and/or professional standing e.g. publications in top journals and keynotes, in addition to non-publication outputs and impact such as open-source tools, technical reports, patents, deployed systems Demonstrated track record of cross-disciplinary collaborations Ability to communicate clearly with domain experts, industry practitioners and general public What SIT offers: An applied research environment, working together with industry partners, to tackle real-world problems SIT values applied research impact and evaluates faculty accordingly on how their research had made a difference to society or industry Opportunities to work on a coherent applied AI research agenda with colleagues across disciplines Access to SIT’s Living Lab Network and Punggol Digital District ecosystem as testbeds for applied research A meaningful role in building Singapore’s applied Aerospace MRO research capability at a pivotal moment Singapore’s exceptional quality of life, competitive personal tax environment, and position as Asia’s leading research and innovation hub How to apply: Candidates should submit the following: Curriculum vitae including grants, publications, patents, and deployed systems Cover Letter Research statement (up to 2 pages) outlining your Aerospace MRO research programme and industry relevance Teaching statement (up to 1 page) on applied learning approach Three key research outputs with impact description