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mobile base, an arm, a gripper, a learned policy, a safety module). Each agent runs its own specialized solver and is coordinated to a common, dynamically feasible plan through distributed optimization and
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the crossover between artificial intelligence and Archaeological Prospection. Archaeological prospection faces an unusual combination of challenges: data are sparse and unevenly distributed, observations
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has access to 300m2 of labs for conducting experimental research and is supported by a state-of-the-art 800m2 cleanroom. With 11 tenured scientists and as many as 70 PhDs, postDocs and senior
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an unusual combination of challenges: data are sparse and unevenly distributed, observations are uncertain, and decisions about where to survey unfold sequentially under significant time and cost constraints
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algorithms to store, manage, and interpret data. To meet the challenges, database software needs to communicate efficiently both with the application layer and with the underlying hardware platforms, and
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with other PhD candidates and postdocs, and opportunities for research to stay with partners, both nationally and abroad, through its international network. This PhD position “Distributed Optimization
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algorithms to improve the performance of scientific applications Researching digital and post-digital computer architectures for science Developing and advancing extreme-scale scientific data management
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than 18,500 people, including over 14,000 students and 4,000 researchers from more than 120 different countries. PostDoc Position at EPFL on Frontier Artificial Intelligence Research General The Artificial
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). The successful candidate will contribute to the modeling, simulation, and co-design of next-generation Quantum-HPC (QHPC) architectures, with particular emphasis on integration of quantum and HPC distributed
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environments. Clean public repositories or released source code from past publications is a strong plus. Algorithmic Breadth: Familiarity with probabilistic machine learning, distributional reinforcement