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creative candidates addressing fundamental questions in biomedical research through the lens of molecular evolution. A central requirement is the integration of evolutionary theory in experimental
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conservation. We study the evolutionary origins and maintenance of biodiversity, conserving and restoring native ecosystems, and issues of global change such as managing natural resources within urban ecosystems
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. Demonstrated experience designing analytical frameworks, and experience using machine learning algorithms and Bayesian statistics within the R-language. Demonstrated experience managing project workflows and
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biomarker data. Develop predictive models and algorithms to identify risk factors, disease markers, and potential therapeutic targets for Alzheimer’s disease. Implement machine learning models to improve
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models. Knowledge base of multivariable and vector calculus, as well as geometric algebra. Familiarity with machine learning and algorithms. Knowledge and proficiency with protein Large Language Models
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science and engineering domains. Responsibilities will include: ● Design, develop, and deploy ML/DL algorithms for domain science and engineering fields ● Support RAD Collaboratory research on national
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, Machine Learning, Data Science, Security and Privacy, Parallel and Distributed Computing, Deep Learning, Internet of Things (IoT), and Algorithms. Successful candidates will hold a joint appointment with
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natural resource conservation. We study the evolutionary origins and maintenance of biodiversity, conserving and restoring native ecosystems, and issues of global change such as managing natural resources
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public trust in media. Platform Design, Policies, and Civic Engagement: Investigating how social media platforms’ algorithms, moderation policies, and content recommendation systems shape civic
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evolutionary origins and maintenance of biodiversity, conserving and restoring native ecosystems, and issues of global change such as managing natural resources within urban ecosystems. Posting Details Special