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Field
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processing, characterization, and mechanical testing. A campaign layer, driven by Bayesian optimization, decides which experiment to run next. The postdoc will own the system architecture below that layer
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developed methods will be applied in iterative prospective drug-discovery cycles, with a serine protease from the complement system serving as a real-world lead-optimization case study. The successful
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developed methods will be applied in iterative prospective drug-discovery cycles, with a serine protease from the complement system serving as a real-world lead-optimization case study. The successful
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Optimization: Resolving hardware-specific performance and numerical precision challenges across diverse GPU environments. Architecture Design: Leading the design of new loss functions, model architectures
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project targets building accelerators for problems that are not efficiently handled by digital systems, such as AI training, real-time optimization, etc. Key Responsibilities: Defining detailed experimental
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drug discovery (e.g. generative models, PK models, lead optimization). Description of work/duties: Working in a team-based environment to solve scientific problems Active collaboration with
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resilience proof of concept developed together with Swedish and Finnish partners. Who we are looking for The following requirements are mandatory: A doctoral degree in Electrical Engineering, Computer
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optimization, and application-driven performance analysis for HPC, scientific Artificial Intelligence (AI), and scientific edge computing. We are a leader in computational and computer science, with
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management and in computer architecture domains, our ability to analyze data still falls behind the unstoppable data collection rates. Data-intensive applications are increasingly more demanding in
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opportunity to work with the world's first exascale system, the Frontier supercomputer, and collaborate with experts in machine learning, optimization, electric grid analytics, and image science. The