120 requirements-engineering-"https:"-"https:"-"https:"-"https:" "https:" PhD positions at Monash University
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Computer Science, Data Science, Robotics, Mechatronics, or Software Engineering , with demonstrated knowledge in machine learning, algorithms, and programming. Prior exposure to reinforcement learning
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Computer Science, Data Science, Robotics, Mechatronics, or Software Engineering , with demonstrated knowledge in machine learning, algorithms, and programming. Prior exposure to reinforcement learning
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should have a strong background in: Data Science, Artificial Intelligence and Machine Learning Python and/or R programming Data preprocessing, feature engineering and statistical analysis Supervised and
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should have a strong background in: Data Science, Artificial Intelligence and Machine Learning Python and/or R programming Data preprocessing, feature engineering and statistical analysis Supervised and
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Technology Engineering, or equivalent, excellent mathematical and analytical skills, excellent skills in AI (i.e., deep learning, RL), excellent communication skills (i.e., both written and verbal), the
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translation into routine primary care and community-based preventive programs. Required knowledge Strong skills in mobile app development and full-stack software engineering ML and AI skills Learn more about minimum entry requirements .
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current year; meet Monash's competitive scholarship selection process requirements ; and meet Monash English language proficiency requirements . This scholarship is also available to students
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Interface Applications", IEEE Transactions on Neural Systems and Rehabilitation Engineering 2019 - "Efficient and Private Scoring of Decision Trees, Support Vector Machines and Logistic Regression Models
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, Artificial Intelligence and Machine Learning Python and/or R programming Data preprocessing, feature engineering and statistical analysis Supervised and unsupervised machine-learning techniques Deep
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Interface Applications", IEEE Transactions on Neural Systems and Rehabilitation Engineering 2019 - "Efficient and Private Scoring of Decision Trees, Support Vector Machines and Logistic Regression Models