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of digital data in DNA. -Contribute to designing and optimizing methods for DNA digital data storage in nanomaterials. -Collaborate with interdisciplinary teams, including molecular biologists, chemists
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supercomputer, the world's first exascale computing system. This is a unique opportunity to engage in transformational research that advances the development of AI-ready scientific data, optimized workflows, and
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, you will become part of a successful team of PhD students and postdocs in a constellation of local theoretical and experimental groups. For this postdoc project, we seek a dynamic and motivated
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motivated post-doctoral associate (or research scientist for experienced candidates with 5 years of post-PhD experience). The candidate will work under the supervision of Future-GRID TecH director Dr. Mohamed
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successful team of PhD students and postdocs in a constellation of local theoretical and experimental groups. For this postdoc project, we seek a dynamic and motivated candidate with an interest in
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optimization. -Develop and test multi-agent and distributed control systems using technologies such as IEC 61499 and real-time simulation. -Use robotics, autonomous systems, and digital twins to automate and
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validation and Pileup modeling), the MET High-Level Trigger validation, optimization and performance studies, and to the heterogeneous computing where the focus will be to work on to the current efforts
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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 1 month ago
. The work will apply state-of-the-art three-dimensional atmospheric chemistry and circulation models, together with advanced statistical techniques (optimal Bayesian, Markov Chain-MonteCarlo, etc.) to solve
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Excited to shape the future of hearing aids? Ready to combine your expertise in Signal Processing, Optimization and Machine learning with Perception and Intelligibility? Job description Good hearing
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computational research in accelerator science and technology. The focus is on developing and applying machine learning (ML) methods for accelerator operations and beam-dynamics optimization in advanced