Peking University

Schemes of Service: Faculty Division: Infocomm Technology Employment Type: Permanent, Fixed Term Singapore Institute of Technology (SIT) invites applications for Academic Staff positions in Operational Technology (OT) Cybersecurity. We are building a distinctive capability at the intersection of cybersecurity, engineering systems, critical infrastructure, and applied learning. We welcome outstanding candidates who can help SIT strengthen its capability in OT cybersecurity, especially colleagues who can work with real engineering systems, brownfield environments, cyber-physical operations, and industry partners. Appointment may be made at a rank aligned with experience and profile. Academic Staff positions in SIT comprise both Faculty and Professional Officers. They play distinctive yet complementary roles in advancing SIT's mission in education and applied research. Faculty provide academic leadership in the design, delivery, and continual enhancement of programmes, ensuring academic rigour and industry relevance, while Professional Officers, as technical specialists, embed current and emerging industry practices and support practice-oriented delivery. Together, they strengthen SIT's applied learning environment through hands-on, real-world approaches that enhance students' industry readiness. In applied research, faculty lead translational research and innovation in collaboration with industry, bridging knowledge creation with practical application, while Professional Officers contribute deep technical expertise and industry experience to translate concepts into implementation, prototyping and deployment, thereby ensuring impactful and industry-relevant outcomes. Why join SIT SIT is Singapore's first University of Applied Learning and the University for Industry. Our students learn in authentic environments, work on real problems with external partners, and undertake substantial workplace learning through the Integrated Work Study Programme. Our approach to education is explicitly competency-based, with a focus on demonstrated capability, workplace relevance, and flexible pathways for learners. SIT's Punggol campus is a testbed for the systems around which this role is built. The campus runs on Singapore's first Multi-Energy Microgrid (MEMG), which is the largest microgrid in Singapore and the first MEMG on a university campus in Southeast Asia. The MEMG was built and operated with SP Group, integrating solar PV, thermal, and distributed energy resources into a live, sandbox-grade environment for energy transition research. The campus also houses the Energy Efficiency Technology Centre (EETC), established with the National Environment Agency, whose Energy Efficiency Training Facility is a first-of-its-kind integrated industrial systems plant in Singapore — giving staff and students a working industrial process environment for development, applied research, and hands-on training. Alongside these, the Centre for Intelligent Robotics at Punggol Digital District, together with the SIT–NVIDIA AI Centre, gives staff and students authentic cyber-physical environments in which to develop, integrate, and stress-test new technologies under realistic operating conditions. In parallel, SIT works closely with the maritime ecosystem on decarbonisation and digitalisation. Through its Future Ship and System Design Programme and partnerships with DNV's Maritime Decarbonisation & Autonomy Regional Centre of Excellence, Seatrium's Offshore & Marine Digital Learning Lab, RINA, and MPA-linked initiatives, SIT engages directly with shipowners, yards, classification societies, and regulators on zero-emission and autonomous vessels, shore charging and future-fuel bunkering, digital twins, and AI-enabled remote operations. Taken together, these platforms, the deep industry partnership network across the energy and maritime sectors and next-generation AI and robotics use cases, provide an exciting ecosystem of real assets, operators, and operational problems to which security, resilience, and safe-operation research can be applied, tested, and translated into practice. Areas of interest in OT cybersecurity This search prioritises practitioners. We are looking, first and foremost, for candidates with substantial, hands-on experience defending real-world industrial environments as asset owners, operators, integrators, consultants, or responders, who can bring that operational experience, engineering constraints and sector-relevant problems into the classroom and the lab. We welcome accomplished academics, but the strongest candidates will have spent significant time on plant floors, in control rooms, and inside live OT networks and environments. Candidates should be able to translate that field experience into rigorous, hands-on teaching, authentic learning experiences, and useful, industry-relevant outcomes. We are interested in the security, resilience, and safe operation of real-world industrial systems, particularly in the maritime and energy sectors, including secure applications of robotics and AI in these operational environments where critical systems must continue to operate safely under degraded or contested conditions, and cyber decisions have physical, operational, and safety consequences. Relevant areas include: secure deployment and brownfield hardening of OT systems already in live operation safe modernisation, segmentation, remote access, and control integration in industrial or infrastructure environments OT testing, experimentation, and validation environments that reflect real production constraints OT incident response, resilience, degraded operations, and safe recovery OT asset discovery, dependency mapping, criticality analysis, and operational risk visibility security of cyber-physical systems, controllers (e.g., PLCs, RTUs, HMIs, DCS, IEDs, power converters and charging systems), embedded devices, industrial protocols (e.g., Modbus, CAN, DNP3, OPC-UA, EtherNet/IP, PROFINET, BACnet), and industrial control networks consequence-aware security analysis for complex OT systems-of-systems Key responsibilities You will contribute to SIT's mission through a combination of teaching, curriculum development, applied scholarship, and external engagement in OT cybersecurity. Depending on rank and profile, responsibilities may include teaching undergraduate, postgraduate, and continuing education learners designing modules, labs, case-based learning, and authentic assessments developing hands-on OT cybersecurity learning environments, training assets, scenarios, and exercises supervising capstone projects, industry-linked projects, and graduate students where appropriate contributing to programme development across pre-employment and continuing education offerings developing relevant and durable institutional OT cybersecurity capabilities for Singapore's local ecosystem, including industry, infrastructure operators, and the broader workforce delivering impact through projects and partnerships with industry, government, infrastructure operators, engineering organisations, and professional communities contributing to interdisciplinary initiatives across engineering, cybersecurity, computing, AI, safety, and applied systems domains providing thought leadership towards shaping local practice, policy input and advisory roles Teaching and educational contributions At SIT, strong teaching means building learning experiences that are hands-on, rigorous, and close to practice. In OT cybersecurity, this may include secure architecture and deployment exercises, brownfield hardening projects, industrial protocol analysis, controller and embedded-system security labs, OT visibility and asset discovery activities, incident response simulations, resilience and recovery exercises, or projects shaped by real operational, safety, and engineering constraints. Our educational model values authenticity, workplace relevance, and the ability to assess whether learners can perform, not only whether they can recall. You should be able to contribute to curriculum design, develop modern teaching materials, mentor students well, and work with colleagues to improve programmes over time. Experience with applied learning, competency-based education, workplace learning, engineering education, or industry-based training will be valuable. Candidates will be able to leverage SIT's campus, laboratories, and industry partnerships to create realistic learning and experimentation platforms where secure engineering systems, production-informed validation, and operational resilience can be taught, tested, and translated. The strongest candidates will connect technical cybersecurity depth with operational and domain relevance, and work across engineering, cybersecurity, safety, operations, and management domains. Research / scholarly / translational contributions SIT values scholarship that moves beyond publication alone. We are interested in research and scholarly work that can be tested, translated, adopted, or used to solve real problems. In OT cybersecurity, this may take different forms: applied research with infrastructure operators or engineering partners; translational work that leads to methods, tools, datasets, validation approaches, or deployable capabilities; practice-based scholarship grounded in operational experience; sector-facing evaluations; or interdisciplinary work that brings cybersecurity into real systems and real environments. Relevant themes may include secure brownfield modernisation, OT systems-of-systems validation, resilience and recovery under cyber disruption, safe use of operational data, dependency and criticality mapping, engineering-informed risk assessment, and consequence-aware incident response. The strongest candidates will show a clear line of sight between their expertise and tangible external value. Candidate profile / qualifications We welcome applications from candidates with strong academic, applied, or practice-oriented profiles. You should have Doctorate or a Master's degree, with substantial and credible industry achievement, in engineering, cybersecurity, computer engineering, electrical and electronic engineering, control systems, systems engineering, industrial automation, information security, or a closely related field, or an equivalent combination of industry standing, recognised OT/ICS certification, and substantial, credible operational achievement expertise in one or more OT cybersecurity domains relevant to this search, backed by substantial hands-on experience in real industrial or infrastructure environments a strong interest in student learning and engagement, and keenness to adopt flipped classroom methods, team-based learning, and authentic assessments for learning strong communication skills and a collaborative mindset interest in working across disciplines and with external partners The following will strengthen an application experience in real-world engineering, industrial, infrastructure, government, or operational technology environments operational knowledge and experience with brownfield OT systems and processes, industrial control systems, building systems, utilities, manufacturing systems, transport systems, energy systems, or other cyber-physical environments practical understanding of OT architecture, industrial protocols, controllers, embedded systems, remote access, segmentation, monitoring, or lifecycle constraints, familiarity with frameworks such as IEC 62443, NIST SP 800-82, NERC CIP, IACS UR E26/27, and the SANS ICS Five Critical Controls. experience in OT incident response, resilience planning, recovery, safety-critical operations, or operational risk management professional certifications or recognised practice credentials relevant to cybersecurity, OT security, industrial systems, engineering safety, or infrastructure operations, such as GICSP, GRID, GCIP, GIAC, CISSP, or ISA/IEC 62443 credentials for candidates at the levels of Associate Professor / Professor, the ability to lead programmes, mentor colleagues, build external partnerships, and help shape institutional capability Application Submission Applications will be accepted until the position is filled. All applications must be submitted electronically through the SIT careers website using the Apply Now button. As part of your application, please submit the following materials cover letter curriculum vitae research, scholarship, or applied practice statement teaching statement Questions about this opportunity may be directed to the Cybersecurity Faculty Search Committee Chair at SIT_CYBERSECURITY_ACADEMIC@SINGAPORETECH.EDU.SG. Only shortlisted candidates will be contacted.

Liverpool John Moores University (LJMU) is a distinctive, unique institution, rooted in the Liverpool City Region and with a global presence. Our students and staff, past, present, and future, are the beating heart of our city and can be found in every corner of every industry and community. We couldn’t exist anywhere else and have shaped the city in which we belong. Working with the people of Liverpool to improve lives and support communities is at the heart of why we were founded and why we exist today. Supporting the delivery of Strength and Conditioning (S&C) services to university athletes and other partners. To develop, manage and lead on the delivery of an effective strength & conditioning programme for the LJMU Performance Sport Programme. This will include designing, delivering and evaluating strength and conditioning sessions for the LJMU Performance Sport athletes based in Liverpool by developing, implementing and evaluating strength and conditioning programmes that reflect scientific principles as they relate to adaptation, periodisation, peaking, tapering, injury prevention, rehabilitation, recovery and athlete screening/assessment. The role will also include management of S&C practitioners that are responsible for S&C delivery to TASS scholars and externally contracted athletes. In return, we offer an excellent benefits package including generous annual leave entitlement, pension scheme, induction and development support as well as family-friendly policies. This is an exciting time to join the university as we deliver the LJMU Strategy 2030 and its vision of LJMU as an inclusive civic university transforming lives and futures, by placing students at the heart of everything we do. If you feel that this is the role you have been looking for and your skills and experience can make a real difference at LJMU, we look forward to hearing from you. LJMU is an equal opportunities employer and welcomes applicants from all backgrounds and communities irrespective of age, transgender status, disability, gender, sexual orientation, ethnicity and religion or belief. All our appointments are made on merit. Please note all of our vacancies will be closed to applications at midnight on the advertised closing date, unless otherwise stated.

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 support and contribute to an industry innovation research project. The Research Engineer will work closely with the Principal Investigator (PI), Co-PI, and the research team to develop deep learning-based computer vision algorithms and software for object detection, classification, and segmentation. Key Responsibilities Participate in and manage the research project together with the PI, Co-PI, and research team to ensure timely achievement of project deliverables. Undertake the following specific responsibilities in the project: i. Develop, train, and optimise deep learning models for object detection, classification, and segmentation using real-world datasets. ii. Design and implement software modules to integrate the models into a working system prototype. iii. Perform data annotation. iv. Conduct experiments, analyse results, and iterate models for improved accuracy and efficiency. v. Prepare project documentation, technical reports, and academic publications. vi. Collaborate with industry partners and contribute to technology transfer efforts. Job Requirements Possess strong technical knowledge and hands-on experience in: Deep learning frameworks (e.g., PyTorch, TensorFlow) Deep learning models (e.g., YOLO, U-Net, EfficientNet, ResNet, FPN, Fast R-CNN) Computer vision techniques and algorithms Python and relevant libraries (e.g., PyQt, OpenCV, NumPy, scikit-learn), particularly for developing Windows desktop application software incorporating deep learning models Hold at least a Bachelor’s degree in Computer Science, Electrical/Electronic/Software Engineering, or a related field. A Master’s or PhD degree in relevant areas will be advantageous. Familiarity with the following areas is advantageous: Participation in Kaggle competitions, showcasing practical problem-solving and model development skills Model deployment (e.g., ONNX, TensorRT) Edge computing or embedded vision systems (e.g., NVIDIA Jetson Nano) Real-time processing and GPU acceleration Experience working on industry R&D projects Key Competencies Able to build and maintain strong working relationships with team members, stakeholders, and external partners Self-motivated and committed to continuous learning and improvement Proficient in technical writing & presentation, research reporting, and academic publication Possess strong analytical, problem-solving, and critical thinking skills Demonstrate initiative and ownership in carrying out tasks independently