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, policy, energy conversion, new business models, techno-economic and life cycle analyses, machine learning, optimization, AI, intelligent networks, among others. The PDF will join a project in collaboration
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SUMOylation, transcription factors, or chromatin dynamics. Expertise in machine learning or statistical modeling for biological data. Knowledge of enhancer-promoter interactions and 3D genome organization. All
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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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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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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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data, spatial modelling, multivariate statistics and/or machine learning, and relevant coding languages (e.g. R, Python), including a sound understanding of FAIR data principles, data management and
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of at least some of the following: – Extensive independent research experience – Creativity and independence – Experience analyzing hyperspectral data and developing machine learning models - Genetic
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decision tool based on SLP/NLP, and utilizes large language models. The project will focus on interaction with clinicians, with a goal of closing the gap between foundational research in machine learning and
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illness. We have a large team working on developing technological solutions for these applications. We are seeking a computer science researcher to take an active role in developing novel machine learning