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Qualifications Education: Master’s degree in machine learning/AI, physics, applied mathematics, or a related quantitative field. Experience: Demonstrated experience developing AI/ML and deep-learning methods
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, ground-based systems, multispectral and hyperspectral imaging, LiDAR, satellite systems, automated greenhouse phenotyping, and related technologies. • Apply AI, machine learning, statistical modeling, and
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experiments. Conduct experiments related to deepfake detection and computer vision. Prepare, preprocess, and analyze image and video datasets. Train and evaluate deep learning models using performance metrics
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related field at the level of a master degree Programming skills (Python) and experience with common machine learning platforms Experience with deep learning, computer vision, medical image analysis
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Certified. QUALIFICATIONS: REQUIRED: · Doctorate in Computer Engineering, Electrical Engineering or closely related field Ability and commitment to effectively teach and mentor undergraduate and
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sitting at a computer workstation for extended periods of time and performing tasks with repetitive motions (such as typing). Also involves some risk of injury from moving, lifting or positioning patients
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and illegal logging detection. Working at the intersection of computer vision, deep learning, geospatial analysis and remote sensing, the postholder will collaborate closely with academic and
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models for high tech industry applications. Your results are expected to be published at leading international venues in machine learning, computer vision, robotics and radar, such as NeurIPS, CVPR, ICRA
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statistical and machine learning, deep learning, chemometrics, multimodal data fusion, computer vision, uncertainty-aware modeling, stochastic control, optimization, and deployable edge-to-cloud decision
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software for neuroimaging processing and analysis (FSL, AFNI, in-house software tools). Prepare documentation of IBIC data analysis workflow and other experimental practices. Enter MRI, diagnostic, and