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blood samples to advance patient care. This role will involve developing computational models (statistical, machine learning, etc.), and using them to perform high throughput analysis of clinical data
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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | about 1 month ago
data analysis methods to study biological memory circuits and their applications to machine learning. Building on recent work from the Fiete Lab, the role focuses on identifying principles of biological
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and/or gender identity or expression, marital status, military status, national origin, parental status, partnership status, predisposing genetic characteristics, pregnancy, race, religion, reproductive
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resonance imaging data among other imaging modalities, machine learning methods for prediction, treatment effect estimation, and contribute to understanding brain biomarkers of Alzheimer’s disease and their
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enable physics-informed AI agents that accelerate discovery in catalysis science. The successful candidate will focus on the development and application of machine learning and AI approaches to analyze
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at the interface of biostatistics, machine learning, and biomedical data science. This mentored postdoctoral position is designed to support the development of an independent research trajectory in methodological
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. This project aims to establish a comprehensive surveillance platform that combines Illumina short-read and PacBio HiFi long-read sequencing with advanced machine learning approaches. The research will involve
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U.S. Department of Energy (DOE) | Washington, District of Columbia | United States | about 6 hours ago
, and Machine Learning) or Data Science. Stipend $700.00 Weekly Point of Contact Renee Eligibility Requirements Citizenship: U.S. Citizen Only Degree: High School Diploma/GED, Associate's Degree
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Description Our Commitment Texas A&M University is committed to enriching the learning and working environment by promoting a culture that respects all perspectives, talents & lived experiences. Embracing
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surveillance and preparedness planning using multiple modeling approaches. The successful candidate will develop and implement statistical and machine-learning models, integrate multi-source ecological datasets