TU Dortmund University

Ref.: 534202 Work type: Full-time Department: Department of Sociology (30400) Categories: Professoriate Staff Applications are invited for appointment as Tenure-Track Assistant Professor in the Department of Sociology (Ref.: 534202), to commence in January 2027 or as soon as possible thereafter, on a three-year fixed-term basis with the possibility of renewal and consideration for tenure before the expiry of a second three-year fixed-term contract. The Department of Sociology was founded in 1967 as the first sociology department in Hong Kong. It is based within the Faculty of Social Sciences and encompasses sociology, demography, criminology, and anthropology, fostering an interdisciplinary approach to the study of social issues anywhere in the world. The Department has strong traditions in qualitative research, particularly ethnographic and participatory methods, and has expanded its expertise in quantitative research, social statistics and demography. The 2026 QS World University Ranking rated the Department the 24th among the world’s top 200 universities in the subject of sociology. The Department offers a range of innovative and engaging taught programmes, including undergraduate major/minor programmes in Sociology, Criminology, and Media & Cultural Studies. In addition to research postgraduate degrees, it also offers three taught postgraduate programmes in Sociology, Criminology, and Media, Culture and Creative Cities. Its faculty, research postgraduate students and post-doctoral fellows, are actively engaged in cutting-edge research and excel in producing excellent scholarly research in the social sciences. The Department is also home to the HKU Anthropology Research Network. Information about the Department can be obtained at https://sociology.hku.hk. Applicants should possess a Ph.D. degree in Sociology, Criminology, Anthropology or a related field, and demonstrate a strong commitment to excellence in research, teaching, and academic leadership. Those who are specialised in cybercrime and/or digital harm, with a research background on issues such as surveillance, digital victimology, misinformation, artificial intelligence and crime, and financial crimes (such as online fraud and money laundering) are particularly welcome. A solid research track record in complementing digital methods with theory-driven and empirically grounded work on these topics is an advantage. The appointee is expected to publish research, teach at the undergraduate and postgraduate levels, supervise students at graduate and undergraduate levels, and take on service/administrative responsibilities. A highly competitive salary commensurate with qualifications and experience will be offered, in addition to annual leave and medical benefits. At the current rates, salaries tax does not exceed 15% of gross income. The appointment will attract a contract-end gratuity and University contribution to a retirement benefits scheme, totaling up to 15% of the basic salary. Housing benefits will be provided as applicable.  Applicants should apply online at the University’s careers site (https://jobs.hku.hk) and upload (1) a cover letter, (2) an up-to-date CV with the contact information of three referees, (3) graduate transcripts and certificates, (4) one writing sample, (5) a research plan (two pages), (6) a teaching statement (also two pages) and teaching evaluations, and (7) outlines of two proposed undergraduate courses. Review of applications will start as soon as possible and continue until July 1, 2026, or until the post is filled, whichever is earlier. 

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 Research Engineer will play a key role in automated wildlife identification and classification from trap camera images using cutting-edge computer vision technology. Working closely with the Principal Investigator, Co-PI, and interdisciplinary research team, RE will develop and implement deep learning algorithms to analyze trap camera footage for wildlife monitoring and conservation efforts. Job 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: Develop, train, and optimise deep learning models for wildlife species identification, classification, and segmentation using real-world datasets. Design and implement software modules to integrate the models into a working system prototype. Perform data annotation. Conduct experiments, analyse results, and iterate models for improved accuracy and efficiency. Prepare project documentation, technical reports, and academic publications. Collaborate with industry partners and contribute to technology transfer efforts. Support the design of simple web interfaces or dashboards to visualise CV model outputs, working alongside developers when needed. Contribute to system integration by applying familiarity with backend/frontend workflows, ensuring CV models can be accessed through user-facing applications. Assist in deployment of CV solutions on cloud or edge platforms with basic interface support for end-users. The candidate is to liaise and communicate with any internal or external stakeholders to ensure project deliverables are met and to perform any other adhoc duties assigned by Supervisor.  Technical Requirements: Possess strong technical knowledge and hands-on experience in: Deep learning frameworks (e.g., PyTorch, TensorFlow, Keras) Computer vision models for object detection and classification (e.g., YOLO, R-CNN variants, EfficientNet, ResNet, U-Net) Image processing and computer vision techniques  Python programming and relevant libraries (e.g., OpenCV, NumPy, scikit-learn, Pandas, Matplotlib) Experience with dataset preparation, model training, and performance evaluation Candidates with strong computer vision expertise and proven success in 1-2 substantial CV projects are welcome to apply regardless of domain-specific experience Familiarity with Web/Full-Stack Development: Basic understanding of frontend frameworks (e.g., React, Angular, or Vue.js) Eposure to backend development (e.g., Flask, Django, Node.js) Awareness of RESTful APIs and microservices architecture. General knowledge of database systems (SQL/NoSQL). Experience with cloud platforms (AWS, GCP, Azure) for deployment and scaling Educational Requirements: Hold at least a Bachelor's degree in Computer Science, Software Engineering, Data Science, or a related technical field Master's or PhD degree in Machine Learning, Computer Vision, or related areas will be advantageous Preferred Qualifications: Experience with biological/ecological datasets or wildlife imagery Familiarity with data annotation tools and practices for large-scale datasets Knowledge of model deployment and optimization (e.g., ONNX, TensorRT, model quantization) Experience with edge computing or embedded systems (e.g., NVIDIA Jetson, Raspberry Pi) Background in real-time processing and GPU acceleration (CUDA) Participation in relevant competitions (e.g., Kaggle, computer vision challenges) Experience with version control (Git) and collaborative development practices

Shape the next generation of social work professionals through teaching that makes a real-world difference. Build a meaningful research profile in a discipline committed to lived experience, Indigenous knowledges and social impact. Full-time appointment on a tenurable basis Base salary range: Level B, $118,150 – $139,812 p.a. plus 17% superannuation. About the area The Department of Social Work and Social Policy sits within UWA’s research‑intensive School of Health and Clinical Sciences, recognised for its strong evidence‑based approach to teaching, research and community partnership. The Department offers accredited undergraduate and postgraduate programs, including Bachelor of Social Work (Honours), Master of Social Work, HDR pathways and specialist graduate certificates. Teaching and research are grounded in the integration of Indigenous cultural knowledges and the embedding of lived experience across curriculum and scholarship. About the opportunity Deliver high‑quality, research‑informed teaching across undergraduate and postgraduate Social Work and Social Policy programs, including unit coordination and student supervision. Develop an independent and collaborative research program that contributes to the Department’s strategic priorities and attracts competitive research funding. Play an active role in shaping the future direction of teaching, research and engagement within the Department, School of Health and Clinical Sciences, and the broader profession. About you You hold a PhD relevant to Social Work or Social Policy and are eligible for membership of the Australian Association of Social Workers (AASW). You bring demonstrated university‑level teaching experience, with the capability to design, coordinate and deliver engaging learning experiences across face‑to‑face and online modes. You have an emerging or established research profile, including publications and experience contributing to competitive grant applications. You are experienced in supervising Honours, Masters and/or HDR students and supporting high‑quality research outcomes. You are a collaborative academic who values inclusion, equity and professional engagement and enjoys contributing to a vibrant scholarly community. Note: Employment checks will include information on gender-based violence, sexual harassment, and related misconduct to meet legal obligations. Position description: PD - Lecturer (Social Work & Social Policy) 522981.pdf To learn more about this opportunity, please contact Professor Stephan Lund at stephan.lund@uwa.edu.au How to apply Please apply online via the Apply Now button and address the selection criteria in your submission in a separate document. For information to assist you with compiling statements to answer the selection criteria, please visit Addressing the selection criteria. You may submit a statement describing any personal circumstances relevant to your application. This can include academic/professional interruptions or achievements relative to opportunity. Closing date: 11:55 PM AWST on Monday 11 May 2026 This position is only open to applicants with relevant rights to work in Australia. Benefits (Unijobs Only) Flexible working options. Generous paid leave including parental leave. Up to 17% employer paid superannuation. Salary packaging available. Incremental progression based on 12 months continuous service. Discounted UWA course fees. Discounted health insurance. Employee Assistance Program. About the University The University of Western Australia (UWA) is ranked among the top 100 universities in the world and a member of the prestigious Australian Group of Eight research intensive universities. With a strong research track record, vibrant campus and working environments, supported by the freedom to innovate and inspire, there is no better time to join Western Australia’s top university. Learn more about us. Our commitment to inclusion and diversity UWA is committed to a diverse workforce and an equitable and inclusive workplace. We are committed to fostering a safe environment for all, including Aboriginal and Torres Strait Islander people, women, those from culturally and linguistically diverse backgrounds, the LGBTIQA+ community, and people living with disability. If you require any reasonable adjustments, we encourage you to advise us at the time of application. Alternatively, you can contact us for assistance during the recruitment process. If you have queries relating to your application, please contact the individual named in the advertisement. Alternatively, please contact the Talent team at people@uwa.edu.au with details of your query. To enable a quick response, please include the 6-digit job reference number.