Edith Cowan University

Applications are invited for a full‑time, fixed‑term Research Technician / Specialist Research Technician to join the laboratory of Dr Nela Nikolic in the School of Biological Sciences at the University of Southampton. Appointment will be made at either Research Technician or Specialist Research Technician level, commensurate with the skills, qualifications and experience of the appointee. The role supports a two‑year externally funded research programme focused on the development of phage‑based therapies for biofilm‑associated bacterial infections. The project examines how Escherichia coli withstands antibiotic and bacteriophage treatments, with the aim of contributing to the development of next‑generation antimicrobial strategies. The research spans microbiology, virology, infection biology and single‑cell biology and contributes to international efforts to address antimicrobial resistance. The role holder will work with Dr Nikolic and will engage with PhD researchers, Master’s students, and student interns within the research group. The post involves taking responsibility for defined components of the research programme, alongside contributing to the effective delivery of laboratory‑based research. Key activities include supporting experimental workflows such as phage isolation and host-phage interaction studies, which underpin understanding of bacterial survival and therapeutic development. The role requires applying a broad range of microbiological and molecular biology techniques, for example, bacterial growth assays, genetic manipulation approaches, phage, biofilm and infection assays, and quantitative analyses at both population and single‑cell levels. The post holder will also contribute to experiments involving fluorescence microscopy, flow cytometry, and omics‑based approaches. In addition to experimental activities, the role includes contributing to the operational needs of the laboratory, such as maintaining bacterial and phage collections, adhering to relevant safety and regulatory requirements, and ensuring accurate and systematic recording of experimental data. The role requires strong organisational capability, reliability and attention to detail. The post is based within a research environment with strengths in microbiology, phage biology, biofilms, single‑cell analysis and antimicrobial resistance. The role offers opportunities for technical skill development, interdisciplinary research exposure and career progression. Subject to performance and funding availability, there may be opportunities for extension or further roles. About you: You will have practical laboratory experience in microbiology, ideally involving bacterial pathogens and/or bacteriophages, together with experience using molecular biology techniques. Experience with fluorescence microscopy, flow cytometry or infection‑related models is desirable but not essential, as training will be provided where required. You will be able to work independently and within a research team, demonstrate analytical and problem‑solving capability, and communicate clearly and professionally. Our commitment to you: The University of Southampton provides access to training, mentoring and professional development opportunities. You will be part of a research community that promotes equality, diversity, inclusion, wellbeing and career development, and is committed to a professional and respectful working environment. Enquiries about the vacancy, shortlisting and interviews: N.Nikolic@soton.ac.uk General enquiries: recruitment@soton.ac.uk To apply, please upload your CV and a cover letter outlining how your skills, qualifications and experience meet the requirements of the role.

Ref.: 536158 Work type: Full-time Department: Department of Ophthalmology, School of Clinical Medicine (22300) Categories: Professoriate Staff Applications are invited for appointments as Tenure-Track Clinical Professor/ Clinical Associate Professor/ Clinical Assistant Professor (several posts) in the Department of Ophthalmology, School of Clinical Medicine (Ref.: 536158), to commence as soon as possible, on a four-year fixed-term basis, with the possibility of renewal subject to satisfactory performance and consideration for tenure before the expiry of a second four-year fixed-term contract. Applicants should possess a medical qualification registrable with the Medical Council of Hong Kong, with a specialist qualification in ophthalmology recognized by the Hong Kong Academy of Medicine or its equivalent. Candidates should have proven research capacity and an outstanding track record of active research, including high-impact output, external competitive grants, and awards, along with a strong commitment to clinical excellence and training, as well as experience in undergraduate and postgraduate teaching. They must display strong clinical acumen and skills in the relevant specialty, with demonstrated experience teaching medical students, interns, and trainees, while proven ability to initiate and contribute to basic, clinical, or translational research is essential. Fluency in Cantonese, Putonghua, and English is preferred. Preference is given to those with a track record in research governance, university and hospital administration, while higher academic qualifications (e.g., MD, MS, PhD) are advantageous. Those with less qualifications and experience may be considered for appointment as Clinical Associate Professor or Clinical Assistant Professor. There will be opportunities for academic growth, career development and mentorship, with protected time for educational and clinical research activities, depending on the appointees’ interests and accomplishments. The appointees are expected to conduct research and teaching in ophthalmology, develop and provide regular clinical services and patient care at the HKU Eye Centre and the University’s affiliate teaching hospitals; and contribute to administrative duties in the Department, Hospitals and Faculty. Information about the Department can be obtained at https://www.ophthalmology.hku.hk/ A highly competitive salary 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. A monthly cash allowance will be offered to the successful candidate. Housing benefits will also be provided as applicable. The University only accepts online application at the University’s career site (https://jobs.hku.hk) for the above posts. Applicants should apply online, upload (1) an up-to-date CV and (2) a teaching and research statement, and provide contact information of 3 referees. Review of applications will start from July 12, 2026 and continue until August 13, 2026, or until the posts are filled, whichever is earlier. 

We are looking for a Research Engineer to join our MOE AcRF Tier 1 funded research project at the Singapore Institute of Technology. You will be part of an interdisciplinary team spanning accounting and NLP/AI, working to design, build, and validate a pipeline that detects inconsistencies and contradictions between firms' written financial filings (10-K/10-Q) and spoken earnings call transcripts. The role involves implementing retrieval-augmented natural language inference models and LLMs, processing large-scale financial text data, and supporting empirical analysis of contradiction scores.  Job Responsibilities Drive the technical execution of the research project under the guidance of the PI and Co-PI, ensuring project deliverables and timelines are met. Design and implement the retrieval-augmented NLI pipeline for inconsistency and contradiction detection across corporate disclosures (earnings call transcripts and SEC filings). Perform large-scale text preprocessing, cleaning, segmentation, and indexing of earnings call transcripts and financial filing corpora. Implement claims extraction modules using LLM-based approaches to identify key assertions from corporate disclosures. Build semantic retrieval mechanisms (e.g., Sentence-BERT embedding similarity search) for matching claim-context pairs across disclosure channels. Implement and evaluate contradiction detection models using NLI frameworks and LLM prompting techniques. Aggregate model outputs into firm-quarter level scores and prepare structured datasets for empirical analysis. Support human-in-the-loop validation by coordinating with student annotators and preparing annotation guidelines. Maintain reproducible research workflows using version control (Git) and systematic documentation of processes, code, and findings. Job Requirements Bachelor’s degree or Master’s degree in Computer Science, Information Systems, Data Science, Artificial Intelligence, or a closely related field. Strong programming skills in Python, with experience in relevant libraries such as HuggingFace Transformers, PyTorch/TensorFlow, and scikit-learn. Prior experience or coursework in natural language processing, machine learning, or information retrieval. Familiarity with version control tools (e.g., Git/GitHub) and collaborative development practices. Experience with financial data or financial text analysis will be advantageous. Familiarity with LLM APIs, prompt engineering, and retrieval-augmented generation (RAG) workflows will be advantageous. Good communication skills and ability to work both independently and collaboratively.