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Field
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annotation of these metabolomes using multistage fragmentation (MSⁿ) data, incorporating novel computational methods and strategies (e.g. spectral matching, network-based approaches, machine learning) where
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Methodology The PhD candidate will develop innovative AI models using machine learning and deep learning frameworks. Methodologies will include supervised and unsupervised learning approaches to identify and
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antigens, T cell receptor (TCR) and antigen interactions and their crucial role in anti-cancer immune responses. You'll leverage your strong background in computational biology, machine learning, and
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and Data Analytics in Air Traffic Management Systems. The selected candidate will work on developing innovative optimization and Machine Learning models to address key challenges in the future airspace
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Melbourne CBD campus About the Role We are seeking a Postdoctoral Research Fellow to join RMIT's Materials Modelling and Simulation group to apply classical Molecular Dynamics and Machine Learning approaches
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at the RMIT Melbourne CBD campus About the Role We are seeking a Postdoctoral Research Fellow to join RMIT’s Materials Modelling and Simulation group to apply classical Molecular Dynamics and Machine Learning
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experience in spatial analysis and/or machine learning methods, and an interest in applying these tools to urban and housing policy questions. The Fellow should demonstrate potential for producing high-quality
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to develop and optimize scalable experimental protocols across diverse material families. This role is part of a multidisciplinary team integrating materials chemistry, machine learning, and autonomous
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applies. 2 Fixed-term position available for up to 24 months. Flexible work arrangements can be negotiated with the right candidate. Be part of the Australian Institute for Machine Learning -- the largest
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qualification/experience in a related field of study. The successful applicant will have expertise in statistical modelling, epidemiology or machine learning and possess sufficient specialist knowledge in