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International Research grant, "Next Generation International Research Hub for Algorithms, Combinatorial Optimization and Discrete Mathematics" with project leader Professor Ken-ichi Kawarabayashi (NII
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approaches, the application of meta learning, and the integration of convex optimization layers Increase inference efficiency (e.g., GPU acceleration) and assess the applicability domain of learned algorithms
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Your Job: Energy systems engineering heavily relies on efficient numerical algorithms. In this HDS-LEE project, we will use machine learning (ML) along with data from previously solved problem
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Tiny Machine Learning (TinyML). The role will focus on the design and development of battery‑less, ultra‑low‑power IoT systems capable of executing secure TinyML‑based visual perception algorithms
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Science Center has an opening for a graduate student researcher in Mathematical Optimization for large-scale power systems planning. They will deploy developed optimization algorithms on DOE high
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. The first workstream, AI and democracy, explores how algorithmic systems affect democratic institutions, deliberation, and participation -- probing questions of how AI concentrates or disperses political
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: Design hierarchical models that explicitly capture misspecifications in metabolic models Develop differentiable and scalable inference algorithms using automatic differentiation Implement HPC-tailored
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environments Design and implement reinforcement learning algorithms for control and manipulation, first in simulation and later on real experimental setups Refine a real-time planning and execution architecture
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on the laboratory can be found here. Our group develops machine learning algorithms to automatically generate discoveries from large-scale brain imaging data. We aim to uncover fundamental principles
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machine learning, deep learning, or AI. Solid mathematical, algorithmic, or physics background, distinct analytical skills. Very good programming (Python, C++) and computer (Linux, Windows) skills