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Field
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mixed reality (MR) strategies to blend information derived from machine learning and computer vision processes to the workers' expertise. The objective of this project is to develop and evaluate MR
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This self-funded PhD opportunity explores assured multi-sensor localisation in 6G terrestrial and non-terrestrial networks (TN–NTN), combining GNSS positioning, inertial systems, and vision-based
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-physical system Sensor fusion, perception and big data Cybersecurity, automotive networking Simulations, verifications and validations of autonomous vehicles Human-machine interfaces and interactions Self
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demonstrate suitable experience in computer science, machine learning, robotic vision, or a related field (through a high-quality Honours or Masters degree). The successful candidate must be able to enrol as a
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and small, contribute to a better world. We look forward to receiving your application! Your work assignments We are looking for one PhD student working on generative AI/machine learning, with
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within the broad topics of modelling tool-workpiece interaction in mechanical material removal processes, zero-defect manufacturing, machining system performance characterization as well as on-machine and
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the fields of microsystem engineering, microdevices and microfluidics, intelligent systems programming and modeling, autonomous control and computer vision are preferred. The successful candidate should be
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. The vision of the MICRO-PATH doctoral training unit (DTU) is therefore to tackle these challenges in a focused way and to lay the foundation for establishing the microbiome as a therapeutic and preventative
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informatics. Research strengths include intelligent systems, data analytics, cybersecurity, digital health, sustainability and human–computer interaction. The Faculty of IT at Monash University provides
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laboratory team is likewise highly recognized for its research in computer vision and neuro-inspired artificial learning. Both teams have been collaborating for four years on projects at the interface between