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Field
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Responsibilities: To independently undertake research in data privacy, machine learning, and applied cryptography. To produce research papers and reports as required by the funding body or for dissemination
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. Publish high-quality research in top-tier conferences and journals. Job Requirement: Ph.D. in Computer Science, Robotics, or a related field. Expertise in machine learning and safe navigation algorithms
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to build sequence dependent predictive deep learning models, and physical mechanistic models (thermodynamic and kinetic models etc.). Examples of suitable backgrounds: machine learning, programming
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computer skills for accurate data collection using various hospital EMR systems (primarily EPIC systems) and willingness to learn various Data Capture systems (WebDCU, RAVE, Red-Cap) ability to work on site
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research interests in one or more of the following subfields: scientific machine learning, optimization, deep learning, uncertainty quantification, (Bayesian) inverse problems, reduced order modeling, high
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on leveraging data from permanent forest plot datasets in the Caribbean to build a comprehensive view of tropical tree species in support of conservation and broader management actions. Learning Objectives
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and machine learning to optimize treatment conditions. Contribute to the development of reproducible stress priming methods and assist in transferring knowledge to agricultural stakeholders. Required
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seminars Each Fellow receives $100,000, allocated as follows: $80,000/year for salary $20,000 for moving, immigration, research travel, and computer costs Five one-year fellowship positions are available
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for implementing the model as a computer simulation and analysing it within a health-economics framework using standard computational techniques. The post-holder will also be responsible for writing up the findings
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Australian National University | Canberra, Australian Capital Territory | Australia | about 2 months ago
at ANU. We are a transformative centre for research in artificial intelligence and machine learning, computer systems and software, and theoretical foundations of computing. We span traditional and modern