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machine learning and AI acceleration. Perform performance, power, and area (PPA) analysis of processor and accelerator designs. Publish research findings in top-tier conferences and journals and contribute
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Materials Design - Develop and apply machine learning and AI models (e.g., ML interatomic potentials, generative design, reinforcement learning) to predict and design materials. - Perform first-principles and
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Research Fellow (Software, AI & Autonomous Systems) Required Qualifications PhD in Robotics, Computer Science, Artificial Intelligence, Electrical, Computer Engineering, or related disciplines. Key
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of) a PhD in computer music, product/industrial design, human-computer interaction (HCI), disability studies, mechanical engineering, mechatronics, robotics, design engineering or other relevant field
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pathophysiological mechanisms for acute organ injury research under POI-KB. · Authorship of high-impact first-author or co-author manuscripts in leading informatics and machine learning journals and conferences
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the application of machine learning/artificial intelligence (ML/AI) in environmental health. This project aligns with ATSDR's current strategic initiatives and will provide you with opportunities
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. The position reports to Associate Professor Elizabeth Williams at UNSW School of Science and has no direct reports. Who You Are (skills and experience): A PhD in nuclear science, human-computer interaction
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Science, Materials Informatics, or a closely related discipline. 3–5 years of postdoctoral research experience with a strong publication record. Demonstrated expertise in machine learning for time-series or multimodal
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, including collaboration with industry partners. Experience applying AI, machine learning, or advanced analytics to integrate chemical, sensory, process and experimental data to support innovation and process