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. degree in cognitive neuroscience, neural computation and machine learning, or a related field with a strong background in EEG, functional near-infrared spectroscopy (fNIRS), or computational modeling, as
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Ref.: 532117 Work type: Full-time Department: Teaching and Learning Innovation Centre (47500) Categories: Senior Research Staff & Post-doctoral Fellow Applications are invited for appointment as
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Sciences, Chemistry, Physics, Computer Science, or related disciplines with a strong background in computational methods and machine learning. Experience in materials informatics, high-throughput
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technical skills in next generation sequencing, including library preparation and bioinformatics analysis. Experience in developing bioinformatics pipeline and maintaining computer server would be
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, fluent in written and spoken English, and competent in basic computer database management. Familiarity or experience with gene sequencing or gene manipulation will be an advantage. The appointee will be
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analysis, and proficiency in statistical and computer modelling software (e.g. R, Python, Matlab, and C++) would be advantageous. The appointee will work with a research team to study the methodologies
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informed neural networks (PINN) and explainable machine learning (EML) frameworks; experience in related technologies including large-scale data analysis, deep learning, Python, PyTorch; and the ability
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experience in one or more of the following areas: development of methods for multi-omics data integration, application of machine learning models in life science, single cell data analysis and spatial
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basic principles of statistics, machine learning, and LLMs. Proficiency in at least one programming/scripting language (e.g., Python, R), along with strong experiences in relevant libraries and frameworks
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. Knowledge and skills in mathematics, biostatistics, or advanced statistical techniques in clinical research, database management, and machine learning (AI) will be taken into account. They should have