Hubei Polytechnic University

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. Key Responsibilities Participate in and manage the research project with the Principal Investigator (PI), co-PIs, research team and industry partners to ensure all project deliverables are met. Conduct insights-driven evaluations of existing QA/QC testing practices and identify opportunities to enhance efficiency and reduce redundant testing while maintaining compliance. Curate, standardize and integrate historical and statistical data to form the foundation of a structured database for construction QA/QC and traceability. Design and implement a digital chain-of-custody framework that captures assurance records including mill certificates, fabrication data, inspection results and carbon footprint information. Develop, train and validate AI models to automate QA/QC processes, enabling real-time verification, anomaly detection and consistency checks across records. Develop a cloud-based verification platform for digital data collection and real-time analytics. Develop and apply non-destructive testing (NDT) methods and data-driven approaches to determine material properties of reclaimed steel. Investigate geometric imperfections, accumulated deformations, and their impact on structural performance, particularly for compression members. Develop data-driven reusability assessment platforms integrating NDT data, machine learning models, and RFID-enabled traceability systems. Prepare and draft technical reports, conference/journal papers, and presentation materials; present research findings to internal reviews, industry partners and stakeholders. Engage with industry stakeholders throughout development, testing and refinement to ensure alignment with real-world requirements. To communicate in any relevant internal or external stakeholders to ensure project deliverables are met. Any ad-hoc duties assigned by Supervisor.  Job Requirements Master’s degree in Computer Science, Data Science, Civil/Construction Engineering, Engineering Systems or equivalent. Strong foundation in data engineering, AI/ML development, and software systems (e.g., data pipelines, cloud services, dashboards, web-based application development). Experience in handling real-world datasets and model development/deployment workflows. Domain expertise in building construction/civil/structural engineering, including familiarity with building management systems, software integration, data collection and analysis. Knowledge of construction QA/QC and quality control workflows (e.g., inspection processes, test documentation, traceability and compliance records) is an advantage. Strong background in structural engineering, structural mechanics, or steel structures. Familiarity with non-destructive testing (NDT) techniques for structural materials is desirable. Experience with structural modelling and analysis tools (e.g., finite element modelling, structural simulation) is desirable. Good interpersonal, written and verbal communication skills, with the ability to work independently and in multidisciplinary teams. Strong analytical and conceptual abilities; able to work both in a team and independently. Able to work under pressure and meet deadlines. Key Competencies Proficient in Python or other equivalent programming languages. Strong foundation in data analytics and data-driven modelling. Able to build and maintain strong working relationships with people within and external to the university. Able to work independently with strong data analytical skills, communication and interpersonal skills. Self-directed learner who believes in continuous learning and development. Proficient in technical writing and presentation. Possess strong analytical and critical thinking skills. Show strong initiative and take ownership of work.

Career Development Fellowships The University is committed to enabling all our colleagues to achieve their full potential. Durham University's Career Development Fellowships are fixed term positions, which include structured development support for early career academics to deliver outstanding education, innovative research/scholarship, and to engage in citizenship activities. The post-holder will be a full academic member of the Department, working alongside world-class colleagues, with the support of a designated mentor. Career Development Fellows will benefit from tailored support to strengthen their skills, will be provided with a range of academic opportunities, and will benefit from protected time to foster different aspects of their career. The Career Development Fellowships will enable early career academics to acquire a strong and well-rounded foundation to support future applications for substantive academic roles at Durham or elsewhere (no guarantee can be given that a permanent role at Durham will be available for the CDF to apply for at the end of the Fellowship).

Associate Professor (Computer Science/Data Science/Software Engineering/AI/Cybersecurity/Quantum Computing) Job Details: Job No.: 492728 School/Unit: School of Information Technology Location: Main Campus Employment Type: Full-time  Duration: Fixed-term Amplify your impact at a world top 50 University Join our inclusive, collaborative community Be surrounded by extraordinary ideas - and the people who discover them[1]  At Monash, work feels different. There’s a sense of belonging, from contributing to something groundbreaking – a place where great things happen. We value difference and diversity, and welcome and celebrate everyone's contributions, lived experience and expertise. That’s why we champion an inclusive and respectful workplace culture where everyone is supported to succeed. Learn more about Monash. The Opportunity A Level D academic is expected to make a significant contribution to all activities of the organisational unit or interdisciplinary area and play a significant role within their profession or discipline. Academics at this level may be appointed in recognition of distinction in their disciplinary area. The appointee will primarily support research and teaching activities in his/her own disciplinary area. S/he will be an innovative researcher with a strong and current record of research funding and international quality publications. Reporting Line: Head of School and Deputy Head of School (Education or Research) Why work with us? Discover the advantages of working with us and why we’re the ideal choice for your career. Explore the benefits we offer here. Position Description  Associate Professor - SoIT *For LinkedIn Users, please click apply to view the position description at our career site Your application Your application must address the selection criteria. For instructions on how to apply, please refer to “How to apply for Monash Jobs”. Enquiries For further enquiries, please email us at meghan.angelicapaul@monash.edu Monash University Malaysia reserves the right to delay or not to proceed with an appointment for the above-mentioned position. Job Closing Date 8 July 2026 11:55 pm MYT