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and image processing. Prior experience with data fusion, machine learning, or super-resolution methods will be considered an asset. Candidates should be motivated to conduct independent scientific
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protocols, ITC will focus on the monitoring and response parts, building on many earlier projects revolving around the use of UAV/drones, computer vision and machine learning, change and damage detection, and
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twin of sperm motility, and utilize it to develop a separation method. Your tasks will include: Performing computer simulations and matching them to experimental data Very close collaboration with
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) and satellite platforms, and surface energy balance models will be used to obtain evapotranspiration (ET); computer vision and machine learning techniques will also be used to identify and count fruits
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excellence through standard and customized omics data analysis, as well as to implement innovative machine learning, data integrative and multimodal solutions. The Center is located in the School of Medicine
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data analysis (e.g., Python, R, C++, MATLAB), computational modeling, imaging and sensor data processing, bioinformatics, systems biology, or biophysics. Familiarity with simulation environments
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to have a strong interest in data analysis, and medical research, along with relevant academic background and skills within medical image analysis and machine learning that will enable them to contribute
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results. You will join the Section for renewable energy and forest sciences and conduct cutting‑edge research using remotely sensed point‑cloud and image data to map forests. The position is part of two
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for this position focused on machine learning (ML). This position offers a unique opportunity to conduct both basic and applied research in collaboration with Brookhaven Lab staff, the Department of Energy (DOE), and
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Computer Vision There is growing trend towards explainable AI (XAI) today. Opaque-box models with deep learning (DL) offer high accuracy but are not explainable due to which there can be problems in