University of Southampton

Ref.: 534905 Work type: Full-time Department: Department of Social Work and Social Administration (30300) Categories: Professoriate Staff Applications are invited for appointment as Tenure-Track Professor/Associate Professor in Mental Wellness in the Department of Social Work and Social Administration (Ref.: 534905), to commence on December 1, 2026 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. Applicants for the Professor position should possess strong academic credentials, outstanding academic leadership capabilities, extensive experience in conducting externally-funded and rigorous field-based research, and a distinguished record of scholarship. Exceptionally outstanding candidates may be considered for appointment on direct tenure terms subject to the University’s approval. The Department of Social Work and Social Administration has five research clusters carrying out cutting-edge research in Evidence-based Social Work Practice, Mental Health, Population Studies, Ageing, and Arts Therapy. All these are geared towards the promotion of wellbeing for all. We have a competent team of professoriate staff, teachers, and researchers working on a wide range of mental wellness issues for different target groups. More information about the Department can be obtained at https://www.socialwork.hku.hk/. Mental wellness is a core research focus of the Department. We are particularly keen to further strengthen our leading-edge team in developing novel non-pharmacological interventions. Applicants should have a shared vision for promoting mental wellness, proven research and publication records, and experiences of collaborations with practitioners. Applicants should hold a PhD (or equivalent) in Social Work with formal professional training in social work, clinical psychology or educational psychology. Possession of a recognised professional social work qualification and eligibility for registration as a social worker are strongly preferred. Substantial post‑qualification practice experience, particularly in mental health, psychosocial, or community‑based service settings, would be a strong asset. Candidates should also demonstrate solid research competence; experience in quantitative, behavioural, or cognitive intervention research will be an advantage. The appointee should be research active, and able to lead and participate in collaborative and interprofessional efforts with colleagues at the University or worldwide. Teaching courses, supervising students at both graduate and undergraduate levels, and administration of academic programmes are other core responsibilities. In addition, any successful candidate who is appointed as Professor will be expected to provide academic leadership by spearheading collaborative and interdisciplinary research and knowledge exchange initiatives, and mentoring the next generation of scholars. A highly competitive remuneration package commensurate with qualifications and experience will be offered, in addition to annual leave and medical benefits. At 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, totalling up to 15% of basic salary. Housing benefits will be provided as applicable. The University only accepts online applications for the above post. Applicants should apply online and upload an up-to-date C.V., a research statement and a teaching statement, and state the contact information of three referees on the online application form. At least one of the referees should comment on your research/teaching ability and your contribution to research/teaching in your Department, while the remaining two should have knowledge of your recent academic or employment experience.  Enquiries about the post can be sent to the Head of Department. Review of applications will start as soon as possible and continue until August 31, 2026, or until the position is filled.

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 deliver on an academic research project funded by HTX, where you will be part of the research team under the project titled: Occupant Tenability and Time to Flashover in Small-Sized Flats (Numerical Study Phase) Under this job, you will develop a CFD modelling platform to study effects of different ventilation factors on an occupant’s tenability when trapped in the bedroom and have a reliable time to flashover within representative HDB units. Job Responsibilities:  Participate in and manage the research project with Principal Investigator (PI), Co-PI and the research team members to ensure all project deliverables are met. Undertake these responsibilities in the project: Assists in co-supervision of final year projects (FYP) or capstone projects students under the Project PI. Meeting the deliverables by carrying out the computational simulations and experimental studies planned for under the scope of work of the projects within the timeframe to meet the deliverables. Assists the PI in drafting of reports, conference proceedings and journal articles based on the outcome of the projects. Prepares and shares fortnightly report of results from the computations with the PI. Carry out Risk Assessment, and ensure compliance with Work, Safety and Health Regulations. Coordinate procurement and liaison with vendors/suppliers. To communicate and liaise with any internal and external stakeholders to ensure project deliverables are met. Any other adhoc duties assigned by Supervisor. Work independently, as well as within a team, to ensure proper operation and maintenance of equipment. Job Requirements: Master or Ph.D (preferred) in Mechanical Engineering, Fire Engineering or related fields. Strong knowledge and experience in flashover/building fire simulation is high advantageous. Hands on experience on experimental studies Familiarity with CFD applications working on industrial related projects using high performance computing clusters would also be advantageous Strong foundation in Fluid Dynamics Strong prior experience in CFD – particularly in building fire combustion and spread. Strong skills in turbulence modelling, CFD mesh generation in complex 3D geometries. Proficient in handling large data sets and the ability to analysis and interpret results. Experience in commercial CFD package such as FDS and ANSYS package is preferred. Candidate preferably should be familiar and have sufficient experience in using FDS and ANSYS FLUENT, Meshing tool and post-processing software. Key Competencies: Proficient in technical writing and presentation Possess strong analytical and critical thinking skills Show strong initiative and take ownership of work

We are seeking a Research Fellow to develop AI-enabled cloud analytics and BIM-based Digital Twin systems for real-time geometry monitoring and control in bridge and viaduct construction. Key Responsibilities Develop cloud-based pipelines to process robotic/ATS survey data Build AI/analytics models for alignment, deflection, and tolerance control Design real-time dashboards and alert systems Develop BIM-integrated Digital Twin for design vs. as-built comparison Implement DfC analytics and what-if simulations for construction decision-making Support project management, procurement of equipment/software, and coordination with stakeholders Prepare technical reports, publications, and project deliverablesp Support system deployment with industry partners and contribute to IP development Requirements PhD in Civil Engineering, Construction Management, or related field Strong skills in AI/ML, data analytics, or digital twin systems Experience with BIM (e.g., Revit/IFC) and data integration Programming experience (e.g., Python, cloud platforms) Ability to work independently and across interdisciplinary teams Experience in sensor data fusion, automation, or robotics in construction is a plus Familiarity with IDD, DfC, or digital twin applications is a plus