University of Dundee

Ref.: 534962 Work type: Fractional / Part-time Department: Faculty of Dentistry (08000) Categories: Teaching Staff Applications are invited for appointment as Fractional Senior Clinical Practitioner in Cariology of the Faculty of Dentistry (Ref.: 534962), to commence as soon as possible on a fixed-term (50% fractional) basis for two years, with the possibility of renewal subject to satisfactory performance and funding availability. Applicants must possess a dental or medical (Bachelor’s) degree or equivalent and a Fellowship by examination (or by formal assessment) or Board Certification in General Dentistry by examination, or clinical specialist status or equivalent. Their clinical expertise should enjoy regional acclaim as evidenced by extensive clinical or teaching experience, regional clinical publications/presentations, and regional recognition of clinical excellence in the comprehensive treatment of dental patients and preferably a higher qualification in Conservative Dentistry, General Dental Practice or Family Dentistry. The appointee will supervise clinical teaching of undergraduate students in the nominated Discipline (including Comprehensive Dental Care), provide clinical service, and assist in managing students’ families of patients. Those with clinical specialist status or equivalent may also provide clinical training to postgraduate students, depending on the nominated Discipline. Enquiries about the duties of the post should be sent to Professor E.H.N. Pow at ehnpow@hku.hk. A highly competitive salary commensurate with qualifications and experience will be offered, in addition to annual leave and medical benefits. The appointment on fixed term 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 successful candidate is required to obtain a valid registration with the Dental Council of Hong Kong. (Note: Applicants who do not hold a local dental license must fulfil the essential requirements under the laws of a jurisdiction outside Hong Kong, China, in order to be eligible for Limited Registration. For details, please refer to the Dental Council of Hong Kong’s website at https://www.dchk.org.hk/en/registration/part_III.html.) The University only accepts online applications for the above post. Applicants should apply online at the University’s career site (https://jobs.hku.hk) and upload an up-to-date C.V. Review of applications will start from April 30, 2026 and continue until May 16, 2026, or until the post is filled, whichever is earlier. The University is an equal opportunities employer and is committed to equality, ethics, inclusivity, diversity and transparency

Semiconductor fabrication is one of the most complex and precision-driven forms of manufacturing. At nanometre scales, even subtle variations in process conditions can introduce defects that degrade device performance, reduce yield, and drive up production costs. Addressing this challenge requires new modelling approaches that can capture the full complexity of fabrication processes and enable optimisation before physical manufacturing begins. This project aims to develop advanced deep learning models capable of predicting fabrication outcomes and guiding fabrication recipe optimisation. By learning directly from experimental and process data, these models will enable a shift from iterative, trial-and-error fabrication towards predictive and data-driven manufacturing. We are seeking a highly motivated Machine Learning Researcher to join a multidisciplinary team of fabrication engineers and AI specialists at the University of Southampton, within the School of Electronics and Computer Science, working in the group of Dr Yasir Noori. In this role, you will work at the interface of machine learning and semiconductor engineering, developing models that predict post-fabrication device characteristics from process parameters. You will engage with complex, high-dimensional datasets derived from real fabrication workflows, including microscopy, spectroscopy, and electrical performance measurements. You will work closely with fabrication engineers to translate physical processes into machine learning models, design and train deep learning architectures, and evaluate their ability to generalise across different process conditions. The models you develop will not remain confined to the research lab, but will be validated experimentally and tested at an industrial scale in collaboration with global companies in semiconductor fabrication and electronic design automation. The position offers a rare opportunity to apply machine learning to an important technical challenge with substantial potential impact. You will also be involved in supervising PhD students and junior researchers and play a central role in shaping the research direction of the team. Your work is also expected to contribute to the development of innovative technologies with a clear pathway to commercialisation through the spinout company Deep Fabrication, to influence how semiconductor manufacturing is approached in practice. The role will provide you with deep exposure to nanofabrication processes, experience working with industry-relevant datasets and problems, and the opportunity to publish in leading journals and conferences. It is particularly well-suited to candidates who are motivated by applying machine learning to real-world systems where the underlying physics is complex and not fully understood. This position is offered for 24 months in the first instance, with the possibility of extension for a further 12 months. To apply, please submit your CV and a cover letter outlining how your experience and interests align with the aims of the project, and provide responses to the short-listing questions.

Job Description SCHOOL OF CREATIVE ARTS ACADEMY OF VISUAL ARTS Part-time Lecturer (25260664) The Academy of Visual Arts is the first-of-its-kind local institution in delivering Arts and Technology education, which is looking for Part-time Lecturer(s) to teach the course(s) at undergraduate level in the following areas: Unreal VR technology Digital media and fabrication Creative computation Any other related disciplines The ideal candidate should hold a master’s degree or above in related disciplines, with 3 years of experience in creative industries with aspects of content development, budgeting, licensing, administration, marketing, or audience engagement, preferably with teaching experience at the university level. A good command of English and Chinese, excellent communication skills, and a strong sense of responsibility are required. Duties may also include supervision of students, marking of examination papers, and any other work as assigned by the Programme Director and Director of the Academy. The initial appointment will be offered on a part-time basis commencing Sep 2026. Re-appointment thereafter will be subject to performance review and course availability. Salary will be commensurate with qualifications and experience. Application Procedure: Applicants are invited to submit their applications at the HKBU e-Recruitment System. Those who are not invited for an interview 8 weeks after submission of the application may consider their applications unsuccessful. Details of the University’s Personal Information Collection Statement can be found at https://hro.hkbu.edu.hk/en/worklife-at-hkbu/employee-favourable-environment.html#privacy-policy. The University reserves the right not to make an appointment for the post advertised, and the appointment will be made according to the terms and conditions applicable at the time of offer. Review of applications is ongoing until the position is filled.