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Field
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, including bioacoustics algorithms developed in the team. What you will do Conducting rigorous research at the intersection of ML and wildlife bioacoustics; Actively participating in regular group and one
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sensitivity analyses across pathology severity, body morphology, camera viewpoint, and environmental conditions Develop a single-camera, home-based markerless system Test the algorithm across lab and home
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curious to deliver work that matters, your journey starts here! The Civil and Environmental Engineering Department at Carnegie Mellon offers a unique interdisciplinary program that enables you to develop
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quantitative modeling techniques and artificial intelligence methodologies in brain diseases. The candidate will work on developing advanced new algorithms, testing and validation, and applications in these data
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quantum chemical methods and machine learning; developing quantum algorithms for computational chemistry on quantum computers; and applying existing and new computational methods to study multiscale
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vision, and/or synthetic data generation for model training. Solid programming skills (e.g., Python) for algorithm development, data analysis, and computational modeling. Experience with, or affinity
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algorithm development and hands-on fieldwork; rigour and reliability in data handling; and strong collaboration across academic and industrial partners. Information This is a full-time position for two years
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will include development of algorithms for heterogeneous computing architectures and implementation of AI/ML in a real-time environment. The candidate will also have the opportunity to conduct
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, devices, and networked systems. It develops community applications, data assets, and technologies and provides assurance to build knowledge and impact in novel, crosscut-science outcomes. The selected
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and efficiency of life sciences research. Developing the algorithms, infrastructure, and governance necessary for such analysis can simultaneously enhance hypothesis generation, computational modeling