Bingol University

FACULTY POSITIONS (AT ALL LEVELS) IN COMPUTER SCIENCE, COMPUTER ENGINEEERING AND INFORMATION SECURITY  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 also establishing strategic live test beds known as ‘Living Labs’ both within and beyond its upcoming Punggol campus to support greater experiential learning and strengthen its applied research offerings. The university’s pool of ICT experts works with multi-disciplinary teams to drive the horizontal tech enablers required to support SIT’s strategic translational research. As a young university, we encourage innovation and seek the right candidates to join us in realising our vision. We offer competitive remuneration, generous employment benefits, access to funding to undertake research of relevance to industry, and opportunities to inspire and nurture the next generation of skilled and work-ready graduates. We currently have positions available at all levels, from Lecturer to Full Professor, in the following areas:  Information Security: Mobile/distributed systems security, Cyber Physical Systems security, security analytics, digital forensics, vulnerability assessment, malware analysis and defence, Privacy-enhancing technologies, AI security. Computer Science and Software Engineering: SDLC, requirement analysis, design, testing, optimisation, analysis, simulation, database, computer graphics, distributed systems, computer vision, video analytics Emerging Fields: IIoT, computational fintech, robot-human interaction, Trustworthy AI, Explainable AI, Large Language Models, Quantum Computing. We have established research teams in operational cybersecurity, AR/VR/HCI and immersive technologies, future transportation solutions, AI, future communications/5G, BCI and embedded systems. We welcome candidates who can complement or strengthen these areas. Successful candidates will join the Infocomm Technology (ICT) Cluster (one of five academic clusters in SIT) as members of a team of accomplished experts who are passionate about higher education and deeply involved in industry transformation through applied research programmes. Faculty in the ICT Cluster undertake funded industry-relevant research, teach courses in Computer Science, Computer Engineering, Information Security and Software Engineering, as well as supervise graduate industry masters and doctorate students in relevant topics. They are also provided with paid leave to undertake consultancy work with industry and/or spend time attached with industry.  We will prioritise collegial and team-spirited candidates with an earned PhD in a related discipline from a reputable university, plus relevant work experience or experience of industry-linked applied research projects. Applications will be accepted until the positions are filled. All applications must be submitted electronically via the "Apply Now" button below. 

2026 Early Career Researcher Fellowships (Commencing 2027) McKenzie | Mary Lugton | Puzey Postdoctoral Fellowships Please ensure to put your application through via this link. Note that applications will not be accepted if not applied via the above link through SmartyGrants. The University of Melbourne invites Expressions of Interest (EOIs) for the 2026 Early Career Researcher Fellowships, with appointments commencing in 2027. This annual round encompasses three competitive schemes: the McKenzie, Mary Lugton, and Puzey Postdoctoral Fellowships. ABOUT THE FELLOWSHIP SCHEMES McKenzie Postdoctoral Fellowships The McKenzie scheme supports outstanding early-career researchers and offers up to sixteen fellowships per year, with at least two reserved for Indigenous researchers. Mary Lugton Postdoctoral Fellowships This year, the scheme supports outstanding early‑career researchers specifically in the fields of Science and Engineering, enabling them to build research momentum and establish the foundations for long‑term academic careers. Puzey Postdoctoral Fellowships In 2026, this scheme supports Australian early‑career researchers who aspire to pursue research careers within the Faculty of Engineering and Information Technology (FEIT). FELLOWSHIP BENEFITS Three-year full-time appointment at Level A.6 plus superannuation Up to $25,000 in project funding Access to mentoring and ECR development programs Commencement between 4 January and 30 June 2027 ELIGIBILITY REQUIREMENTS General Eligibility Applicants must have completed all PhD requirements in a discipline relevant to their application from a university in any country, including Australia and the University of Melbourne no later than 7 May 2026. PhD requirements must be completed by the EOI no earlier than 1 April 2023. Current UoM academic staff may apply if casual, fixed-term, or CRC. An EOI will not be accepted if applicants have not met the PhD requirements by the EOI closing date. Mary Lugton Additional Eligibility Must be an active researcher in Science or Engineering Puzey Additional Eligibility Must be an Australian citizen and resident in Australia Must be interested in employment in FEIT APPLICATION REQUIREMENTS (see the Early Career Researcher Fellowship Guidelines for details) This is a two-stage application process: Stage 1. EOI, and Stage 2. Full Application by invitation only. Applicants must identify an Academic Contact from their intended faculty to support their application. Applicants must discuss their interest with the relevant faculty prior to submitting an EOI. Applicants invited to complete the application require 2 reference letters. Please ensure to put your application through via this link. Note that applications will not be accepted if not applied via the above link through SmartyGrants. KEY DATES 7 May 2026 – Expressions of Interest close 22 July 2026 – Shortlisted EOIs invited to full application and Unsuccessful applicants notified. 19 August 2026 – Full applications due McKenzie Fellows - PD.docx McKenzie Fellows - PD.pdf

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.