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Field
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machine learning and computer vision algorithms • Build tools for photogrammetric 3D reconstruction and defect detection • Evaluate and refine AI models using annotated data and real-world image datasets
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algorithms and AI methods for hardware security evaluation. The roles of this position include: Study literatures on PCB security evaluation, PCB multi-modal image analysis, and datasheet parsing. Data
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Learning Develop computer graphics and machine learning algorithms (e.g., SLAM and Gaussian Splatting), and evaluate their accuracy in scene reconstruction and camera trajectory estimation using videos
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Sessional Instructional Assistant - MAT302H5F - Intro to Algebraic Cryptography (emergency posting)1
University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | 13 days agocryptography, from Euclid to Zero Knowledge Proofs. Topics include: block ciphers and the Advanced Encryption Standard (AES); algebraic and number-theoretic techniques and algorithms in cryptography, including
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(e.g. transcriptomics, metabolomics) Knowledge of machine learning/deep learning algorithms Knowledge of systems biology approaches (e.g. genome-scale modeling) Proficient with R, Python or MATLAB Modes
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to contemporary challenges while mentoring the next generation of leaders. Building on its strong foundation in Optics and Photonics, EECS is expanding its research into quantum algorithms and their industrial
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-time reinforcement learning algorithms to personalise training difficulty for the DRUM-AI training app and help shape the future of AI-assisted rehabilitation. The post holder will be based in
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, implements, and maintains data collection systems and strategies. 3. Performs statistical analysis and apply data mining techniques. 4. Develops algorithms and design experiments to merge, manage, interrogate
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biology or related field Experience with omics data analysis (e.g. transcriptomics, metabolomics) Knowledge of machine learning/deep learning algorithms Knowledge of systems biology approaches (e.g. genome
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, and use smart phone apps to collect passive and active data using a prospective observational cohort study design. We will use this data to develop and validate a personalised risk prediction algorithm