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delivered a state-of-the-art molecular sciences research building. This investment builds on a major recent expansion of our academic staff and investment in our teaching and learning provision, with our
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experience in data-knowledge fusion-driven dynamic modeling of complex structures, specializing in uncertainty quantification of nonlinear systems and intelligent model order reduction methods, with at least
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, immunohistochemistry, EEG/EMG in mice is strongly preferred. Experience in molecular cloning or computational modeling is a plus. The appointee should be organized, self-motivated and be able to work independently as
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, development of complex data visualizations, and demonstrated skill in one or more of the following areas: machine learning, statistical analysis, natural language processing and text analysis; experience
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mitigation, dynamic data integration, validation, and interoperability. The research will involve applying various techniques, such as representation learning methods and transformer-based models, to tackle
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will conduct the lab experiment for RAS system for pollution control in recycled water in aquaculture system. He/she will also use machine learning tools to predict and optimize the RAS system. Job
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, data sciences, or a related subject, ideally with a focus on utilising large-scale health/biomedical data and the application of advanced data analysis methods such as machine-learning. Equivalent
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) utilizing animal models. This position offers an exciting opportunity to study the mechanisms of neuroplasticity and rehabilitation utilizing microelectrode array recording and stimulation techniques
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We are seeking a highly motivated Postdoctoral Research Associate in the field of Theoretical and Computational Chemistry to develop accurate models of time-resolved X-ray photoelectron spectroscopy
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. Desired: Familiarity with statistical and machine learning techniques. Knowledge about molecular biology and/or gene regulation. Experience with nanopore sequencing, Hi-C, ribosome profiling, or CAGE data