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Field
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. The successful candidate will assist in developing novel algorithms and integrating them into robotic platforms, helping to push the boundaries of embodied intelligence in both research and practical deployment
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applications including asset pricing, volatility modelling, risk management etc. Developing the framework of economic world models, with generative AI algorithms such as diffusion models and theory-informed
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reproducible bioinformatics workflows and computational tools to process, analyze, and interpret high-throughput sequencing data, including genomics, transcriptomics, metabarcoding, and metagenomics datasets
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) . About the program/department/team The successful candidate will contribute to an international collaborative research project involving researchers from the Ted Rogers School of Management at TMU and the
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development for both data analysis and machine learning applications, including data modeling and algorithm development and implementation. Software design, development, and testing to support research and
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or engineering sciences including computer science, electrical engineering, or related discipline. Proficiency working with data algorithms (regression, probability, statistics). Experience with machine learning
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are looking for world-class candidates with expertise in the computational aspects of algorithmic design and a strong focus on applying these skills to real-world problems. The successful candidate will be
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the theory of brain-inspired algorithms and apply them to complex, real-world problems. The successful candidate will join an interdisciplinary team of computer scientists, mathematicians, engineers, and
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, computer science, or related field. Experience developing software in C++ or Python for scientific applications. One or more years post-graduate experience developing GNSS signal processing or related algorithms
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potential to mature as research engineer in one of the following research areas will be advantageous. Fast computational electromagnetics (CEM) algorithms in frequency domain; Fast CEM algorithms in time