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Intelligence, Machine Learning, Quantum Technologies, Energy Informatics, and Immersive Technologies. Our teaching programs are delivered across Curtin’s global campuses. Power and Renewable Energy Engineering
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. • be located at the agreed project location(s) and, if required, comply with the university’s external enrolment procedures. Selection criteria Skillset: Proficient in Python, machine learning, and
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Computer Science, Applied Mathematics/Statistics, Robotics, Physics, or related discipline, with an excellent academic record. Demonstrated expertise in computer vision and machine learning, including object
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foundations in applied mathematics, computer vision and machine learning, particularly object detection and tracking. Proficiency in deep learning frameworks (e.g., PyTorch, TensorFlow) and Python ML libraries
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, Applied Mathematics/Statistics, Robotics, Physics or related discipline, with an excellent academic record. Strong foundations in applied mathematics, computer vision and machine learning, particularly
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discipline, with an excellent academic record. Demonstrated expertise in computer vision and machine learning, including object detection, segmentation, and multi-object tracking in challenging conditions such
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Performance . About You The successful candidate will play a key role in the development and validation of computational tools that integrate spatial transcriptomics, algorithmic methods, and machine learning
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Dream. Design. Make. Monash Makerspace is where creativity meets cutting-edge technology — a vibrant hub for innovation, hands-on learning, and real-world problem solving. From student-led projects and
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outputs of a team dedicated to translating discovery into meaningful impact for people living with Friedreich Ataxia. We are seeking someone with a PhD in computer engineering, biomedical engineering
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Computer Science, Applied Mathematics/Statistics, Robotics, Physics, or related discipline, with an excellent academic record. Demonstrated expertise and leadership in computer vision and machine learning research