Universidade Mussa Bin Bique

Lingnan University is one of the eight publicly funded institutions in the Hong Kong Special Administrative Region (HKSAR) of the People’s Republic of China (PRC) and has the longest established tradition among the local institutions of higher education. It is widely recognised for providing quality education with a focus on whole-person development and conducting high-impact research for a better world. Moving forward, Lingnan University is well positioned to take lead as a comprehensive university in arts and sciences in the digital era, with impactful research and innovations. Lingnan University offers undergraduate, taught postgraduate, and research postgraduate programmes in the Faculties of Arts, Business, Social Sciences, and the Schools of Data Science, Graduate Studies and Interdisciplinary Studies. To foster interdisciplinary collaboration and scientific progress, Lingnan University established the Lingnan University Institute for Advanced Study (LUIAS), attracting distinguished scholars from around the world to collaborate with its faculty and students. With traditional strengths in arts, business, social sciences, and interdisciplinary studies, the University aims to equip students with practical knowledge and critical thinking skills to thrive in the future. Subsequent to the establishment of the School of Data Science and LUIAS, Lingnan University is transforming into a hub for global leaders to develop and promote human-centric technology and social policies. Further information about Lingnan University is available at https://www.ln.edu.hk/. Applications are now invited for the following post: Lecturer / Assistant Lecturer Department of Psychology (Post Ref.: 26/31) “Health and Well-being” is the overarching theme for research and teaching at the Department of Psychology (http://www.LN.edu.hk/psy/), with faculty members specializing in positive occupational health psychology, cross-cultural psychology, and experimental social and cognitive psychology. In line with the liberal arts tradition of the University, the Department is also strongly committed to providing high quality teaching and a supportive learning environment to students. General Requirements Applicants should have a Master’s degree or above in Psychology. Applicants are required to provide evidence of quality teaching. Excellent communication and presentation skills are essential. Teaching experience at tertiary institutions and/or administrative experience will be a distinct advantage. The appointee will teach or as a tutor / teaching assistant, assist course instructors in the teaching of basic and advanced courses for the psychology undergraduate programme as well as other psychology-related courses, such as “Introduction to Psychology”, “Human Development and Psychology of Adulthood”, “Abnormal Psychology”, “Social Psychology”, “Social Problem Solving and Decision Making in the Modern World”, “Self-improvement, Personal and Work Success”, “Religious Behaviour, Belief and Experience” and “Research Methods and Statistics for Psychology II”. In addition to teaching duties, the appointee will have to provide support to the operation of the Department, including promotion and publicity of the psychology undergraduate programme, and have a strong commitment to teaching excellence and services to the Department and the University community. Appointment The conditions of appointment will be competitive. The rank and remuneration will be commensurate with qualifications and experience. Fringe benefits include annual leave, medical and dental benefits, mandatory provident fund, gratuity and incoming passage and baggage allowance for the eligible appointee. Appointment will normally be made on contract basis for up to two years. Application Procedure (online application only) Please click "Apply Now" to submit your application. Applicants shall provide names and contact information of at least three referees to whom applicants’ consent has been given for their providing references. Personal data collected will be used for recruitment purposes only. We are an equal opportunities employer. Review of applications will continue until the post is filled. Qualified candidates are advised to submit their applications early for consideration. The University reserves the right not to make an appointment for the post advertised, or to fill the post by invitation or by search. We regret that only shortlisted candidates will be notified.

We are seeking a talented and motivated Research Fellow to join a multidisciplinary team developing a scalable high-power inductively coupled plasma torch for assessing atmospheric re-entry emissions and environmental impact. This project will develop a high-enthalpy inductively coupled plasma torch system to simulate the extreme thermal environments of spacecraft atmospheric re-entry, enabling advanced testing of thermal protection materials and emissions characterisation to support the design of cleaner deorbiting systems. You will work under the supervision of Prof. Minkwan Kim, collaborating with an industry partner and the UK Space Agency. Key responsibilities include: Developing a second-stage plasma accelerator based on magnetohydrodynamic (MHD) principles. Optimising ICP (Inductively Coupled Plasma) torch design for high enthalpy thermal plasma flow. Developing/Validating computational models for a dual stage plasma torch. You should hold a PhD (or equivalent experience) in Plasma physics, Aerodynamics, Mechanical, or Aerospace Engineering. Essential criteria include experience in numerical modelling of plasma flows and discharge, with desirable expertise in plasma diagnostics and RF plasma. Strong communication and writing skills are required. This role offers the opportunity to contribute to cutting-edge research at the intersection of plasma technology and space sustainability. Equality, diversity and Inclusion is central to the ethos in the School of Engineering. In recognition of our continued commitment to improving equality for women in science we were awarded an Athena SWAN bronze in 2013 and 2016. We give full consideration to applicants that wish to work flexibly including part-time and due consideration will be given to applicants who have taken a career break. The University has a generous maternity policy*, onsite childcare facility. The University of Southampton is in the top 1% of world universities and in the top 10 of the UK’s research-intensive universities. The University of Southampton is committed to sustainability and being a globally responsible university and has recently been awarded the Platinum EcoAward. This commits all of our staff and students to take responsibility for managing their activities to minimise harm to the environment, whether this through switching off non-essential electrical equipment or using the recycling facilities.  *subject to qualifying criteria. Informal enquiries may be made to Prof. Minkwan Kim (m.k.kim@soton.ac.uk).

Position Overview: We are seeking an outstanding Postdoctoral Researcher in Artificial Intelligence (AI) and Data Science with expertise in multi-omics data integration for health and precision medicine. The successful candidate will join a multidisciplinary team developing AI-driven approaches to integrate and analyze genomics, transcriptomics, proteomics, metabolomics, and microbiome datasets to uncover biomarkers, therapeutic targets, and mechanistic insights into complex diseases. The project addresses critical challenges in personalized medicine, disease stratification, and multi-modal data fusion, enabling next-generation solutions in precision health and biomedical research. Scientific Challenges Addressed in the Position: Heterogeneity and high dimensionality of multi-omics data requiring advanced AI/ML methods for robust analysis and integration. Data sparsity, batch effects, and missing values across different omics layers and platforms. Cross-omics data fusion and representation learning for comprehensive systems biology modeling. Identification of causal relationships and biomarker discovery through integrative approaches. Time-series and longitudinal multi-omics data analysis for disease progression modeling. Explainability and interpretability of AI models to support clinical decision-making and regulatory compliance in healthcare settings. Scalability and computational efficiency in processing and integrating massive multi-omics datasets from clinical cohorts. Key Responsibilities: Design and implement AI/ML pipelines for multi-omics data integration, including supervised and unsupervised learning methods. Develop deep learning architectures (e.g., variational autoencoders, graph neural networks, transformers) for cross-omics data representation and feature extraction. Apply multi-view learning, transfer learning, and data fusion techniques to integrate heterogeneous omics datasets and clinical metadata. Conduct network-based analysis (gene regulatory networks, protein-protein interaction networks, metabolic networks) to identify key disease drivers and biomarkers. Build predictive models for disease classification, patient stratification, and treatment response prediction. Collaborate with biologists, clinicians, and bioinformaticians for data interpretation and validation of computational findings in clinical or experimental settings. Disseminate research outcomes through publications in high-impact journals, conference presentations, and workshops. Mentor and support the training of graduate students and early-career researchers in AI and multi-omics integration. Required Qualifications: Ph.D. in Bioinformatics, Computational Biology, Data Science, Artificial Intelligence, or a related field. Proven experience in multi-omics data integration, omics data analysis (genomics, transcriptomics, proteomics, metabolomics, microbiome). Strong expertise in machine learning, deep learning, and advanced AI frameworks (TensorFlow, PyTorch, Scikit-learn). Experience with bioinformatics tools and databases (e.g., Bioconductor, Galaxy, KEGG, Reactome, STRING). Proficiency in Python, R, and Unix/Linux-based environments for high-performance data analysis. Knowledge of biological network inference, causal modeling, and graph-based AI approaches. Experience in multi-modal data fusion, representation learning, and heterogeneous data integration. Strong publication record in relevant peer-reviewed journals. Excellent communication skills and ability to work in a multidisciplinary environment. Familiarity with cloud-based computing platforms (AWS, Azure, Google Cloud) and high-performance computing (HPC) environments. Understanding of data privacy, security, and ethical considerations in handling clinical data. Application Process: Interested candidates should submit the following documents in a single PDF: A cover letter outlining their research interests, motivation, and relevant experience. A detailed Curriculum Vitae (CV) with a list of publications. Contact details of two academic referees.