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Research Fellow in Automation and Machine-Learning in Polymer Synthesis Job No.: 665101 Location: Clayton campus Employment Type: Full-time Duration: 2-year fixed-term appointment Remuneration
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This research project aims to address the critical need for privacy-enhancing techniques in machine learning (ML) applications, particularly in scenarios involving sensitive or confidential data
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as a consequence of other desiderata. In the context of machine learning, many approaches propose to learn simple models since judicious simplicity can lead to good predictions [3,1]. These approaches
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Computer Science and/or Chemical Engineering or equivalent; 2) Excellent mathematical and programming skills, in particular in the areas of Artificial Intelligence (AI) and machine learning including deep learning
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. Scientific Contribution Our group has strong publication record of 100+ first or senior author top-tier (ERA ranking A*/A) journals and technical conferences in the machine learning and medical AI field. His
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Adversarial Machine Learning (AML) is a technique to fool a machine learning model through malicious input. Due to its significance in many scenarios, including security, privacy, and health
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, sociological, economic and web data this project will apply machine learning to build a recommendation engine that can provide life advice and predictions to individuals based on their current life situation and
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With success stories ranging from speech recognition to self-driving cars, machine learning (ML) has been one of the most impactful areas of computer science. ML’s versatility stems from the wealth
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based on matched-filter statistics. Detecting the unknown relies on the development of complex algorithms at the forefront of statistics, machine learning, and data science. This multi-disciplinary
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Project description: Nowadays, data-driven machine learning algorithms are well suited to solve real-world problems that require high-level prediction accuracy. However, it seems as if nothing beats