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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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– Corpus Christi has a sense of belonging research environment utilizing geospatial science to support data driven decision making. The institute’s research laboratories develop innovative solutions
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) and developing meaningful scaled score formulas and metrics. The post-doctoral associate will support multiple aspects of this work, including conceptualization and design of the scale scores, computing
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. The Postdoctoral Associate will apply his/her technical skills toward development and implementation of machine learning, computer vision, and other algorithms for analysis of medical images and prognostication as
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, modeling, and analysis for the SCHOLAR project. Develop and deploy the SCHOLAR dashboard, incorporating CHW feedback through multiple design iterations. Collaborate with CHWs and senior research personnel
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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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: Proficiency in algorithm development for autonomous systems. Experience with ROS2, UAV simulation (e.g., AirSim), and real-time system integration. Strong programming skills in C++ and Python. Excellent
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it; as well as have theoretical skills including algorithm implementation/development and data visualization. Experience and interests include designing machine learning pipelines, building web