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biomarkers, clinical, and cognitive variables, genotyping and sequencing data, and MRI brain imaging data on patients with severe mental disorders. In addition, we work closely with national population cohorts
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languages such as Python, MATLAB, or C++, and experience with machine learning frameworks (e.g., TensorFlow, PyTorch) preferred. Familiarity with medical imaging processing and reconstruction techniques
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point cloud data processing, deep learning for time series data prediction, digital twin, geospatial mapping with vehicle and UAV mounted remote sensing systems or robotic systems, crowd simulation
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asset. Familiarity with quantitative text analysis procedures is considered an advantage. Familiarity with digital tools, including computational methods for textual analysis, will be an advantage
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. For modeling, we use both public and proprietary clinical and research data and generate our own repository of digital pathology images. A further focus of our lab is the improvement of digital pathology
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optimization. Basic knowledge of integrated circuit design, including digital simulation and logic synthesis. methods, and other related topics pertaining to fast AI model inference. Experience working in
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). The successful applicant will initially be involved with DOD-funded clinical projects focused on assessment of bone quality using new methods based on digital tomosynthesis imaging, and identifying strategies