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development for data analysis or machine learning applications. Knowledge of artificial intelligence and machine learning techniques. Experience with deep learning frameworks such as PyTorch or TensorFlow is
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evaluate computer vision and machine learning algorithms empirically. Author research articles at an international level for peer-reviewed conferences. Engineer software based on the research outcomes
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: PhD degree in Computer Science, Computer & Electronics Engineering or other related fields Strong background and knowledge in at least one or preferably more of the following fields: Cybersecurity, Deep
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and machine learning. Knowledge of the basics of federated learning and causal inference is highly encouraged. Proven track record in research and development of machine learning algorithms. Proficiency
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one of the following areas: wireless localization, wireless sensing, AI/machine learning for communications, or signal processing. Ability to conduct experimental research independently and
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and biomedical data preparation, feature engineering, statistical modelling, machine learning model development, validation, performance evaluation, workflow prototyping, reproducible analysis
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of wireless communications, edge computing, and machine learning, and who is eager to translate theoretical insights into practical systems. Key Responsibilities Derive and analyse closed-form mathematical
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at the interface of optimization, geometry, and machine learning Designing, implementing, and testing algorithms Engaging in scientific exchange with collaboration partners of the project Preparing reports
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offers a friendly and international work environment Learn more about CQT at https://www.cqt.sg/ The research fellow will work closely with PI Patrick Rebentrost on developing novel quantum algorithms and
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industry-relevant research, teach courses in Computer Science, Computer Engineering, Information Security and Software Engineering, as well as supervise graduate industry masters and doctorate students in