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Field
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machine learning-based systems to integrate more renewable energy into our energy systems and make energy use more efficient. We develop new optimization methods, machine learning algorithms, and
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, including but not limited to algorithms, databases, cloud computing, machine learning, operating systems and security. Jobs Summary: UM6P invites applications for post-doc, in all areas of Computer Systems. A
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of mining processes, mathematical modeling of flows and extraction decisions, and the use of machine learning algorithms to predict ore quality and optimize operational decisions. 2. Key Responsibilities
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candidate will design and develop digital twin algorithms for computational fluid dynamics, contributing to innovative solutions in healthcare software systems. This role involves collaboration with research
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, machine learning, data sciences, algorithms, databases, cloud computing, software engineering, networking, operating systems , security and computational materials science. Description of the position and
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aims to develop formal frameworks and algorithms for eliciting, aggregating, and analysing stakeholder preferences over risk and safety in AI systems. The Research Assistant will support the development
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on applications to complex, dynamic systems. Design and simulate feedback control algorithms for thermo-mechanical systems and related applications. Collaborate with faculty and student teams to support control
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grant. Our program has successfully trained and helped transition multiple postdoctoral fellows into independent research careers. We are currently accepting applications for trainees interested in
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Engineering or a related field. In this role, the selected candidate will be responsible for designing, developing, and optimising algorithms for processing and analysing signals in real-time applications
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optimization algorithms for both estimation and inference. Special emphasis is placed on advancing statistical approaches to address the demands of increasingly complex, high-dimensional, and unconventional data