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scales through component-based parallelism. The focus of the post-doc position is to design and implement this pipeline -- developing native reasoning for rich combinatorial constraints, creating tools
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proficiencies at CMU. Therefore, we are in search of a team member who can optimally get along with a varied population of diverse audiences. We are looking for someone who shares our values and who will support
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opportunity for someone who thrives in an interesting and challenging work environment. Core responsibilities include: Research in the intersection of discrete optimization and machine learning, including
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opportunity for someone who thrives in an interesting and challenging work environment. Core responsibilities include: Research in the intersection of discrete optimization and machine learning, including
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of polyurethane materials in a self-driving laboratory. The successful candidate will work primarily on understanding the structure-property relationships of polymeric materials with a focus on optimization of