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approaches in research design and data analysis, including: Advanced biostatistics and epidemiology, Applications of artificial intelligence and machine learning, and Leveraging diverse data sources (e.g
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Excellent organizational, communication, and teamwork skills Experience with dynamic causal modelling (DCM), machine learning or related computational approaches are an asset Roles and Responsibilities
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, research areas include Operations Research, Information Engineering, Human Factors, and Applied Machine Learning, all of which seek to improve the systems we as humans rely on to navigate our world
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: Current Students in an Undergraduate Program, Current Students in a Master’s Program, Current Students in a PhD Program. Cover letters are mandatory. Please submit it along with your resume. At UBC, we
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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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Science, Artificial Intelligence, Machine Learning, Data Science, Engineering, or a closely related technical field. Demonstrated knowledge of or interest in Indigenous Knowledge Systems and interest in applying IKS
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(inclusive of PhD and/or post-graduate work) in accelerated research and development in the area of development and application of machine learning/deep learning methods for biological or chemical data
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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | 4 days ago
Mississauga (UTM) invites applications for a full-time tenure stream appointment in the areas of Machine Learning, Applied and Theoretical Statistics. The appointment will be at the rank of Assistant Professor
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for this position are as follows: PhD in Forest Ecology, Entomology, or a closely related field, with a focus on geospatial modeling, invasive species dynamics, and applied machine learning for pest risk assessment
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) platforms used in machine learning, big data and artificial intelligence (AI) based applications (CPUs, GPUs, AI accelerators etc.) require high power demands with optimized power distribution networks (PDNs