37 phd-studenship-in-computer-vision-and-machine-learning Fellowship positions at Monash University
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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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. 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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for drug discovery. Publishing peer-reviewed research and contributing to industrial software tools. About You To be successful in this role, you will have: A PhD in machine learning, computer
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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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neurocritical care research The Opportunity We are seeking a Research Fellow - Data Science professional with strong expertise in machine learning, deep learning and high-frequency physiological signal analysis
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computation in science and engineering; Advanced materials and manufacturing; Energy and environment; Future cities; and Life sciences We are seeking an individual passionate about undertaking research in
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to work independently and collaboratively. Advanced planning, time management, and written communication skills are essential, along with proven computer literacy and proficiency in relevant software and
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, and may utilise iterative algorithms, machine learning and high-performance computing. Through the Monash Centre for Electron Microscopy, opportunities exist to acquire large experimental datasets using
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establish and achieve goals, while your excellent written and oral communication skills will support collaboration and dissemination of research outcomes. You will also bring advanced computer skills, with
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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