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Field
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discovery challenges. Qualifications: PhD in bioengineering, computational biology, machine learning, systems immunology, or related discipline, obtained within the last 5 years, by the time of
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Integrated Planning & Learning and Reinforcement Learning in non-deterministic and partially-observed scenarios. The methods will be evaluated on physical robots. The ideal candidate would have: A PhD (or
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Associate/Research Fellow with expertise in Artificial Intelligence (AI)/Machine Learning (ML), pedagogical research in Institutes of Higher Learning (IHLs), and web/mobile application development (both
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; ongoing learning and development opportunities to grow your career; an inclusive and supportive culture and environment to work in, both online and on campus. Who are we? Deakin is a cutting-edge public
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important for renewable energy production and production variability will be an advantage. Knowledge of machine learning or optimization will be an advantage. Applicants must be able to work independently and
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strong, demonstrated interest to conduct academic research in a relevant field Interest in legal research Interest in causal inference and social science Experience with machine learning / deep learning
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: Computer Architecture Algorithms and Optimization Health Research Human-Computer Interaction Machine Learning and ML Foundations Machine Perception Natural Language Processing (including Information
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. Desirable: Proficiency in scientific programming (e.g. Python) and familiarity with data science and machine learning techniques. Experience with geochemical analytical techniques and working in a laboratory
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-driven models to optimize solvent systems. Mentoring the training and learning of PhD and undergraduate students on sustainable biomass conversion topics Assist in report/document/further grant writing
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”. Qualifications Applicants should: (a) have a PhD degree in Remote Sensing, Geomatics, GIS, Computer Science, Photogrammetry or a related field, and must have no more than five years of post-qualification