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are in particular targeting development of data-driven high-performance computing techniques for unbiased discovery of generative models & theory and algorithms for network inference with special reference
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algorithmic advances and aim at publishing them at these top venues. More details about the position can be found here. As a KAUST postdoc or researcher, you will have access to state-of-the-art research
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in simulation, prediction, and analysis of large-scale and complex fluid systems. Special emphasis will be directed toward incorporating high-performance computing, advanced algorithms, machine
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industry partners. Design, implement, and validate advanced reinforcement learning models. Utilize reinforcement learning and evolutionary algorithms to discover new chemical materials. Publish and present
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, risk assessments and contractor work schedules. Establish effective working relationships with the relevant KAUST departments to ensure that the delivery of facilities to the NCL are timely, maintained
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using a combination of multimodal imaging, computer vision, and lab automation platforms that govern entire workflows (e.g. ThermoFisher momentum software scheduling Hamilton liquid handlers and high-end
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to cleanliness and HSE standards. Assist in the procurement of lab supplies and equipment. Assist group members to build or develop tools and equipment. Working Conditions: Flexible working schedule focused
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-supervise master and Ph.D. students. Support the PI with grant proposals and coordinate collaborations Flexibility to adjust to changing schedules and tasks as needed Perform other tasks assigned by the PI
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a need for this (e.g., reference letters), and a Zoom interview will be scheduled.
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and in a team setting Flexibility to adjust to changing schedules and tasks Other Duties: Please note this job description is not designed to cover the complete list of duties or responsibilities