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advanced many-body methods, high-performance computing, and machine learning approaches. The successful candidate will play a leading role in developing computational methods and high-performance algorithms
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(MERCE). The main objective is to develop safe planning and reinforcement learning algorithms with various degrees of confidence for variants of Markov decision processes. More precisely, we will develop
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High-Energy Physics (HEP). We seek highly qualified candidates with interest and experience in ML algorithms including unsupervised techniques, time-series modeling, and clustering algorithms
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, high-performance computing (HPC), and computational sciences. Major Duties/Responsibilities: Participate in: (1) design and implementation of scalable DL algorithms for atomistic materials modeling
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research associate position in AI for science. The Learning Systems Group seeks a postdoctoral researcher specializing in federated learning and privacy-preservation algorithms. The successful candidate will
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trigger reconstruction architectures for future particle collider experiments, based on deep learning models distributed across multiple hardware processing stages. The mission of this position, based
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for machine learning, with research topics ranging from decentralized and federated optimization, adaptive stochastic algorithms, and generalization in deep learning, to robustness, privacy, and security
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quantum information, develop techniques for quantum control and measurement, build quantum computing hardware and software, and explore novel applications. Our main interest is to propose quantum algorithms
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. We have multiple NIH-funded projects to study the influence of FDA-approved GLP-1R agonists on drug seeking and reinstatement in rodents. A newly funded NIH R01 has the goal to determine who may be
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the objects. Many real world networks have a multidimensional nature such as networks that contain multiple connections. For instance, transport networks in a country when considering different means