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Field
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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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genetic knockouts in yeast and mammalian cell lines, and protein purification. Job Responsibilities: 35%: Computational algorithm development and data analysis 35%: Design and conduct experiments with yeast
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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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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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and deploying UAV simulation environments using AirSim or its derivatives. Designing and implementing navigation, planning, and obstacle avoidance algorithms using ROS2. Conducting simulation studies
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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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vision, controls, cyber-physical systems and their security, hardware security, and machine learning and their security. The work will include algorithm design, prototype implementation (e.g., in Matlab
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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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interaction physics, precision calculations of (g-2) and PDF determinations, and the development of new algorithms, among others. Position Description The successful candidate will be expected to conduct