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telecommunications, radio and wireless systems is required. Knowledge of radio frequency propagation principles, antenna principles, telecommunications transmission media, telecommunications signals, design, analysis
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learning algorithm for multi-omics integration 3) Maintenance of server / database (Linux environment) 4) Assisting other team members in data analytics 5) Presenting work in at least one conference in
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data sources such as UK Biobank and eventually come up with algorithm useable for the early detection of Alzheimer’s disease (AD) and Parkinson’s disease (PD). Nature of Work: In this project, we will
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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 | 16 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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radio medium, radio communication system examples. Link budget: cable losses, propagation loss, antenna gains. Basic concepts of propagation: path loss, multi-path propagation and fading. Raleigh and
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developing machine learning algorithms specifically designed for medical imaging applications. In addition, performs analysis of tissue images of cancer using machine learning methods that have been prototyped
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Science Specialist will: 1) review empirical data and algorithms applied to the price, end-use, fishing effort and layer 3 databases and develop code that imputes missing data to complete gaps in
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of Biomedical Engineering, please visit https://www.bme.ubc.ca/ . HuMBL, a research lab within the SBME, specializes specifically in wearable sensor technologies and algorithm-driven health analytics to enhance
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, including algorithms, complexity, cryptography, and logic. The candidate's qualifications, experience and overall market demand will determine a candidate’s final salary offer. The salary for this position
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learning models, including their strengths, deficiencies, and strategies for (hyper)parameter optimization. Prior use of Bayesian optimization or other relevant active learning algorithms is preferred