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Field
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Responsibilities: Conduct research on the design and analysis of scalable machine learning systems using convex/nonconvex optimization and federated learning methods. Develop algorithms and prototypes
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machine learning methods, particularly large language models (LLMs), to marketing research. Applicants are invited to contact Prof. Edward Lai at telephone number 2766 7141 or via email at edward-yh.lai
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strong analytical skills and desirably some computer modelling experience, and an ability to work in a multidisciplinary team and engage confidently with partners. You will have a track record of
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to undertake world-leading research in the design, integration and Edge-implementation/testing of multimodal machine learning models. Your experience in real-time implementation of federated AI and Edge-based
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characterization. Experience or interest in conducting interdisciplinary research, particularly in the intersection of machine learning and materials informatics. Ability to work effectively in an interdisciplinary
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to the advancement of AI applications in biological sciences. This role presents a unique opportunity to work with pangenomic datasets while exploring the application of Large Language Models (LLMs) and machine
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with a strong background in computational modelling or scientific machine learning. You will also have the ability to conduct independent research and a developing track record of publications in
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organoids will be plus. Dry lab: Highly motivated candidates with a PhD/MD degree in bioinformatics, genome science, systems biology, biomedical informatics, computational biology, machine learning, data
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computational materials science techniques (DFT, MD, machine learning force field modelling) with data-driven approaches. Work with team to design and implement high-throughput experimental workflows for rapid
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on resistance mechanisms in breast cancer, glioblastomas, colon cancer, and lung cancer. Advancing precision oncology through machine-learning models: We integrate multimodal patient data, including multiomic