437 web-programmer-developer-"https:"-"https:"-"https:" positions at Carnegie Mellon University
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artificial intelligence with a focus on machine learning in autonomy and uncrewed systems (multi-domain). Strategy: You’ll work with AI Division leaders and colleagues to plan, develop, and carry out
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on the technical implementation and experimental validation of the tools. Document research, development, and experimental results through written reports and presentations. Other duties as assigned. Adaptability
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operationalizing AI for robust, secure, scalable, and human-centered mission capabilities prepare our customers to be ready for the unique challenges of adopting, deploying, using, and maintaining AI capabilities
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innovative microfluidic technologies. Responsibilities: Designing and conducting experiments to study microbial communities using microfluidic platforms. Developing and optimizing microfluidic devices for high
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also expected to develop the backend electronics for neural recording and stimulation. This is part of an interdisciplinary project involving multimodal recording and stimulation from cells in the gut
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curious to deliver work that matters, your journey starts here! The Civil and Environmental Engineering Department at Carnegie Mellon offers a unique interdisciplinary program that enables you to develop
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". The primary purpose of this position is to develop and train Large Language Model (LLM) agents to solve software engineering tasks by solving the "cold-start" problem in Reinforcement Learning (RL). Core
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operationalizing AI for robust, secure, scalable, and human-centered mission capabilities prepare our customers to be ready for the unique challenges of adopting, deploying, using, and maintaining AI capabilities
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to help move beyond traditional scalar reward signals by developing models that generate natural language insights to explain human preferences. The intern will assist in implementing pipelines
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Responsibilities Developing algorithms to link enhancers with their cis-regulatory gene targets Simulation studies and real-data benchmarking Working with and analyzing biological data Developing data Implementing