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Field
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., cores, memory hierarchy, accelerators, interconnects) and software-level decisions (e.g., task mapping, scheduling, compiler optimizations), which together lead to a combinatorial explosion of possible
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to develop and optimize scalable experimental protocols across diverse material families. This role is part of a multidisciplinary team integrating materials chemistry, machine learning, and autonomous
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Computer Engineering (ECE) program of the ECE department at the Northeastern University Seattle campus. Course topics include High-performance Computing, VLSI Design, Advanced Machine, Learning Combinatorial
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from outstanding candidates with expertise in Operations Research, Management Science and Business Analytics with a focus on fundamental research in combinatorial and stochastic optimization for business
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. You will draw on ideas from Bayesian optimization and Bayesian deep learning, generative modelling, high throughput screening, and combinatorial synthetic chemistry. Responsibilities and qualifications
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candidates with expertise in Operations Research, Management Science and Business Analytics with a focus on fundamental research in combinatorial and stochastic optimization for business applications. In
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are exploring which methods are suitable under which conditions to solve combinatorial optimization problems better than purely classical methods. Possible topics for a master's thesis include, for example
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with chemists and materials scientists is essential to ensure that the developed methods make optimal use of domain expertise and integrate fully into “human in the loop” workflows. This post is
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essential to ensure that the developed methods make optimal use of domain expertise and integrate fully into ¿human in the loop¿ workflows. This post is available for two years. Keywords: Geometric Deep
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essential to ensure that the developed methods make optimal use of domain expertise and integrate fully into “human in the loop” workflows. This post is available for two years. Keywords: Geometric Deep