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Field
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Description The Quantum Information team at UMass Amherst is involved with modeling and optimization of quantum hardware, as well as development of new modeling methods and algorithms, in collaboration with
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Models with Algorithmic Reasoning Tasks We are seeking a postdoctoral researcher to contribute to our lab’s mission of aligning machine learning (ML) models with algorithmic reasoning tasks. Our goal is to
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conditions, identification of vulnerabilities, and development of resilience enhancement strategies. Contribute to the design, development, and implementation of new models, methods, and algorithms
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prioritize multiple tasks and projects in a fast-paced office environment, meeting deadlines and project requirements. Excellent verbal and written communication skills. Excellent project management and
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University Hospital, Duke Regional Hospital, Duke Raleigh Hospital, Duke Health Integrated Practice, Duke Primary Care, Duke Home Care and Hospice, Duke Health and Wellness, and multiple affiliations
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writing C++ and PyTorch. Training and debugging RL agents. Imitation Learning algorithms for robotics or autonomous vehicles. Prior work combining RL with human data or feedback. A track record of code
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learning algorithms into professional software with an intuitive user interface, incorporating feedback from CHWs through iterative design and evaluation cycles. The selected candidate will be part of a
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will apply state-of-the-art machine learning algorithms and custom disease-relevant genomic datasets (e.g., coronary artery single-nucleus chromatin accessibility and RNA sequencing) to develop targeted
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the following objectives: 1. Characterize 3-D Urban Structure and Change: Utilize data from multiple remote-sensing platforms and deep learning algorithms to generate high-resolution maps of 3-D urban structure
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and deploying UAV simulation environments using AirSim or its derivatives. Designing and implementing navigation, planning, and obstacle avoidance algorithms using ROS2. Conducting simulation studies