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and illegal logging detection. Working at the intersection of computer vision, deep learning, geospatial analysis and remote sensing, the postholder will collaborate closely with academic and
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image analysis, data processing and machine-learning-based modelling. More about the position The main purpose of the fellowship is research training leading to the successful completion of a PhD degree
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network (MINDnet)”. The PhD project “Efficient learning algorithms for closed-loop optogenetic control of neural circuits” aims to develop an optoelectronic interface with neural circuits, that optically
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MACQUARIE UNIVERSITY - SYDNEY AUSTRALIA | North Ryde, New South Wales | Australia | about 22 hours ago
differential geometry, algebraic geometry, etc.) or for computer science (such as machine learning, linear logic, etc.). While the position start date is flexible, the successful applicant must have completed
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1. A PhD degree in a relevant area, e.g., Chemical Engineering, Control Engineering, Computer Science, Systems Engineering, etc. 2. The candidates should have strong math, modeling, programming, and
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lab experiments Publish results in top-tier journals in related fields Assist in mentoring PhD students Job Requirements: PhD degree in mathematics, systems engineering, or machine learning Expertise in
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the reliability of proposed protective measures. The emphasis can be adapted to the student's background, including machine learning, electrical interfaces, mechanical module design or control
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and memory systems. This effort is truly trans-disciplinary, drawing on biodesign/biotechnology, machine learning, and interaction design. This project builds on groundwork already underway in our group
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multimodal data analysis is preferred; experience with R, machine learning, or advanced statistics is a plus. Training and Research Environment: The successful candidate will receive interdisciplinary training
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sensing systems (wearable, ambient, RF, distributed), edge and on-device data processing and machine learning, resilient distributed architectures for sensing, sensor-data fusion and analytics