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- UNIVERSITY OF SYDNEY
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Field
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energy systems and demand response; process and energy system modeling; optimization and model predictive control; and applications of artificial intelligence and machine learning to energy systems
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machine learning and AI to improve inversion, interpretation, and autonomous monitoring. Publish peer-reviewed research and contribute to external funding proposals. Mentor graduate students, undergraduate
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Devices (including applications to Health), Electrical Engineering Systems (including Communications, Information Theory, Machine Learning), and Computer Engineering (including Embedded Systems, Security
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work environment. Be able to prioritize and quickly learn new techniques and work independently, with careful attention to detail. Be accessible by email. Preferred Qualifications Familiarity with
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decisions. Adheres to University and unit-level policies and procedures and safeguards University assets. Develop various machine learning and data mining models including deep convolutional neural networks
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advance knowledge and improve the human condition. Explore how we are tackling critical challenges at the forefront of biology and medicine, and shaping a healthier, more sustainable future for all. Learn
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of generative AI tools, use of large language models, machine learning, and ethical frameworks for AI implementation. Ability to apply AI to interdisciplinary research or developing AI models
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opportunity to collaborate and learn from experts researching, developing, and testing emerging technologies in marine energy and/or ocean-based industries. You will conduct research at both your academic
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on type, size, location of data centers in the US, their electricity and water demand, carbon emissions; exposure to air pollution. ● Develop and/or apply methods for causal inference and machine learning
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National Energy Technology Laboratory (NETL) | Morgantown, West Virginia | United States | about 2 months ago
permeability of fluid flow through hydrate-bearing sediments, • High resolution visualizations of hydrate distributions and fluid migration in porous media under in situ conditions, and • Machine learning