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Field
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contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will be working primarily with
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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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data, metabolomics and/or proteomics. Develops robust pipelines for data annotation, analysis, and quality control. Creates analytical algorithms and tools to address scientific questions with big data
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investigate how machine-learning based algorithms can be used to personalize the user experience. The goal of this personalized user experience is to enable each individual user to discover their own
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optimizing PIC algorithms for modern heterogeneous architectures, including CPUs, GPUs, and other accelerators, the project seeks to achieve unprecedented efficiency and resolution in plasma simulations
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algorithm sensing, navigation and control of robot systems, including mobile robots and robot arms/manipulators; designing and implementing algorithms for machine learning, computer vision, and/or estimation
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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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motivated Post-Doctoral Associate to join our team with a strong background in robot control, machine learning, and differential geometry to work on the development of advanced algorithms to enhance
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needs. Expertise in several areas among the following is desired: radio system modeling, transceiver hardware design, algorithm design and simulation, interference analysis, spectrum auctions and markets
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-of-the-art semantic and instance segmentation algorithms for 3D and 4D microtomography data. The project has a particular focus towards analysing fibre-based materials but will also consider other material