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, and facilitate sensor optimization to achieve a robust and dependable condition monitoring, predictive maintenance, and prognostics and health management (PHM) regime for the aircraft systems over
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data, spatial modelling, multivariate statistics and/or machine learning, and relevant coding languages (e.g. R, Python), including a sound understanding of FAIR data principles, data management and
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have a background in qualitative research and community engagement. An interest in patient engagement and participatory action is a benefit, and either experience in, or interest in learning mixed
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modelling, satellite data assimilation, multivariate statistics, and machine learning. Prior experience with model and satellite products for mapping and understanding SM-dependent hazards (like floods
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are essential. Preferred Qualifications: Prior experience with machine learning (ML) in high-energy physics is highly desirable, though not required. Appointment Details: The position is expected to be based
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without regard to race, color, sex, gender identity, sexual orientation, religion, creed, national origin, ancestry, age, protected veteran status, disability, genetic information, military service
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available in the Geometric Machine Learning Group at Harvard University, led by Prof. Melanie Weber. This role offers an opportunity to perform research on Riemannian Optimization. The ideal candidate has a
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to the advancement of AI applications in biological sciences. This role presents a unique opportunity to work with pangenomic datasets while exploring the application of Large Language Models (LLMs) and machine
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Pay Grade/Pay Range: Minimum: $48,600 - Midpoint: $60,800 (Salaried E7) Department/Organization: 214251 - Electrical and Computer Eng Normal Work Schedule: Monday - Friday 8:00am to 5:00pm Job
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data to develop a suite of national level vegetation indices capable of long term tracking and monitoring of vegetation quality, density, planting intensity and other suitable characteristics. Although a