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-based prediction. This includes instrumenting a high-power APS torch with video and acoustic sensors, developing machine learning algorithms for feature extraction, and building predictive models
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. Qualifications: PhD in machine learning, with experience in applications in computer vision or medical image analysis. Strong publication record in top venues (e.g., CVPR, MIDL, MICCAI, IPMI, PAMI, TMI, MIA
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initiatives, or with programming language experience and experience in machine learning and health informatics. An understanding of the digital health space, is expected but not essential. Must have previously
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spatial omics datasets. The position will also contribute to multi-modal data integration efforts that combine imaging, genomics, and machine learning approaches. Key Responsibilities Data Processing
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approaches (based on functional programming abstractions) to optimize the implementation of machine learning models and other digital signal processing algorithms on a specific FPGA architecture to fit within
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Science, or a related field. Strong programming skills in Python, R, or related languages for data analysis and machine learning. Experience with genomics data analysis, health informatics, or computational
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datasets, including time-series analysis and machine learning applications. Use spatial statistical tools to relate remote sensing and in-situ observations. Proficiency in geospatial software platforms
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data analytic approaches such as artificial intelligence (AI) and machine-learning to address this complexity are of particular interest. McGill University has an international reputation in excellence