Shunan University

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.

1 x full time (scope to negotiate a smaller time fraction), fixed term (2 years) position available Salary Academic Level A ($80,755 - $109,536) + 17% Superannuation Based at the City, but may be required to work and/or be based at other campuses of the University About You The Research Fellow will join the research team led by Dr/ Amy Gelmi and contribute to collaborative research across the School and affiliated Research Institutes. Working in alignment with the University's research strategy, the appointee will undertake innovative, high-impact research in bionanotechnology and regenerative medicine. This nationally recognised program aims to advance fundamental understanding and translational applications within the discipline. Funded by the Australian Research Council Discovery Project, "Understanding Transient Cellular Response to Electrical Stimulation (DP250101190)," the role will focus on developing cutting-edge methodologies to investigate live cell responses to electrical stimulation with high spatial and temporal resolution. The Research Fellow will design and implement advanced bioinformatics pipelines to interrogate stem cell biology using multi-omics approaches, including RNA sequencing, proteomics, and secretomics datasets. Responsibilities include integrative analysis of complex molecular data, interpretation of cellular response mechanisms, and close collaboration with experimental researchers to uncover pathways regulating stem cell function and fate. The successful candidate will be supported to build an independent research profile through structured mentorship in competitive grant writing and opportunities to contribute to, and lead, fellowship and project applications. They will play a key role in planning and delivering high-quality research projects, establishing productive research networks within RMIT and with local and national partners, and producing impactful research outputs that strengthen the School's research excellence and reputation. We are looking for candidates with their knowledge and experience in the key focus areas: Essential Expertise in tissue engineering and regenerative medicine, ideally working with human mesenchymal stromal/stem cells, and/or induced pluripotent stem cells. Expertise in bioinformatics, including RNA-sequencing, proteomics, secretomics, and pipeline development. Evidence of research outputs including publications, conference contributions and/or technical reports in the field. Ability to work autonomously whilst displaying a strong commitment to work in a team environment, including the demonstrated ability to confidently and effectively work with colleagues, project team leaders, and industry partners. Demonstrated ability to meet deadlines and effectively manage varying workloads and respond to changing priorities as required. Demonstrated high level of written and oral communication skills. Preferable Experience in eukaryotic cell culture/tissue culture. Nanoscale characterisation expertise including Electron Microscopy and/or Atomic Force Microscopy. Electroactive biomaterial experience, including electrochemical characterisation and synthesis. Expertise with advanced graphing and/or data analysis software (Prism, Origin Pro, Matlab etc) Success in research funding and/or experience in grant development and writing. A good understanding of appropriate statistical analysis techniques. Qualifications Mandatory: PhD in Biology/Biotechnology/Chemistry/Biomedical Engineering with experience in mammalian cell culture. To review specific key selection criteria and mandatory qualifications required for each role, please review Position Descriptions below or Aleksandra Besia, Senior Advisor at Aleksandra.besia@rmit.edu.au Research Assistant, Bionanotechnology and Regenerative Medicine Please Note: Appointment to this position is subject to passing a Working with Children and National Police Check. To Apply Please submit your CV and covering letter and address the Key Selection Criteria for this position by clicking on the 'Apply' link at the top of this page. Applications close on Monday, 20th of April 2026. About RMIT University RMIT is a multi-sector university of technology, design and enterprise with more than 96,000 students and close to 10,000 staff globally. The University's mission is to help shape the world through research, innovation and engagement, and to create transformative experiences for students to prepare them for life and work. https://www.rmit.edu.au/about https://www.universitiesaustralia.edu.au/university/rmit-university Why work at RMIT University Our people make everything at the University possible. We encourage new approaches to work and learning, stimulating change to drive positive impact. Find out more about working at RMIT University, what we stand for and why we are an Employer of Choice. We want to attract those who will make a difference. View RMIT's impressive standings in university rankings. https://www.rmit.edu.au/about/facts-figures/reputation-and-rankings Bring Your Whole Self to Work We are better for our diversity. When we listen to those who are different to us, or challenge us, we grow stronger together. When we feel like we belong, we can achieve so much more. We respect each other, embrace our differences and build a sense of belonging in our team and beyond. We are proud that our dedication to diversity has been recognised by a broad spectrum of external organisations. Visit our Linkedin Life pages to learn more and see some of our most recent wins. RMIT has a strong dhumbali (commitment) to the employment, development and retention of Aboriginal and Torres Strait Islander people within a culturally safe environment; we strongly encourage applications from Aboriginal and Torres Strait Islander peoples. At RMIT, we are committed to supporting adjustments throughout the recruitment and selection process, as well as during employment. We actively support and encourage people with disability to apply to RMIT. To discuss adjustment requirements, please contact Dani (Senior Talent Advisor), via talentsupport@rmit.edu.au or visit our Careers page for more contact information - https://www.rmit.edu.au/careers RMIT is an equal opportunity employer committed to being a child safe organisation. We are dedicated to attracting, retaining and developing our people regardless of gender identity, ethnicity, sexual orientation, disability and age. Applications are encouraged from all sectors of the community and we strongly encourage applications from the Aboriginal and/or Torres Strait Islander community. We are a Circle Back Initiative Employer - we commit to respond to every applicant.

Technical Manager (Finance) in the Department of Finance The Technical Manager (Finance) will provide support to researchers in assessing the feasibility and logistics of running their papers or in collecting primary as well as secondary data. The Technical Manager will help acquire and maintain servers for digital data infrastructure and also support researchers in accessing and using university resources efficiently, e.g., preparing customised data sets for secondary analysis or help onboarding researchers to high-performance financial computing facilities. The role holder will explore potential for linkages to other research and data hubs on the administrative and programme levels on a regular basis. There will be opportunities for the Technical Manager to engage with exciting topics in contemporary finance, e.g., in asset pricing, banking, corporate finance, quantitative risk management, climate finance, financial technologies, to enhance their ability to work with finance colleagues across a broad spectrum of topics.