30 phd-studenship-in-computer-vision-and-machine-learning Fellowship positions at Monash University
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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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within the Environmental Informatics Hub and reporting to the Director, you’ll help lead research into sequential decision-making under uncertainty, such as reinforcement learning and adaptive management
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Participate in seminars, workshops, and conferences related to combinatorics and graph theory The successful candidate will hold a PhD in Mathematics or a closely related discipline, with a strong background in
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Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description We are looking for a Research Fellow who will contribute
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. We are currently seeking a Research Fellow with experience in AI and machine learning research and development, with a focus on any or all of following application areas: Computer vision Generative AI
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international peers working on the program, industry and government stakeholders, and funding bodies. Exploring, leading and coordinating opportunities for new research proposals, initiatives, or collaborations
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, cerebrovascular physiology, neuroinflammation, and translational drug discovery. About you A PhD in Pharmacology, Molecular Biology, Biomedical Science, or a related discipline. Strong hands-on experience in
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evaluate methods via experiments, benchmarking, simulation and/or real‑world data. The successful candidate will have: A PhD in Statistics, Data Science, Computer Science, Mathematics, or a related field
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to optimise instrument performance. The successful applicant will have demonstrated understanding and knowledge in the theory and application of S/TEM and a PhD or equivalent qualification and will possess
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. Your expertise includes machine learning techniques such as Bayesian optimisation, and you’re comfortable working with experimental data, high-performance computing environments, and (ideally) thin film