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the galaxy from their sources to the Earth. A developed Monte Carlo computer code is used to study the acceleration process by plasma shocks, while mathematical models have been designed to describe how
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. Description: Our group uses high resolution ground-based optical and near-infrared solar spectra to diagnose and model different manifestations of stellar activity. Nearby sun-like stars are a crucial sample of
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of data as well as empirical and theoretical modeling of the solar interior and atmosphere, including solar activity and the solar cycle. A program of research is being carried out to understand the coupled
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University of California Riverside School of Medicine | Riverside, California | United States | 5 days ago
-model, peer mentor, and advocate for the Internal Medicine residents. - The PGY4 Internal Medicine Chief Resident position requires a commitment to fulfilling academic and administrative responsibilities
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captured from UAVs. The research will address the design of AI models capable of combining heterogeneous sensor modalities, including RGB, thermal, LiDAR, acoustic arrays, GPR, and X-ray backscatter
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cancer therapeutics and biomarker development to strategically develop our portfolio of preclinical cancer models and ex vivo approaches. Your goal is to enhance our mission to validate and credential new
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materials synthesis and growth, characterization, novel probe development, and theory; 2) Modeling in order to enhance computation technology; 3) Modeling the behavior of skyrmion magnetic interfaces; and 4
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School graduates over a thousand students who are ready to take on great ambitions and challenges. For more details, please view: https://www.ntu.edu.sg/eee We are looking for a Research Associate to
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, and machine learning models for functional genomics research in mycobacteria. Responsibilities Responsibilities include: Develop and maintain Django-based web applications and databases for sharing
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) invites applications for a fully funded 4-year PhD program in Process Systems Engineering. The project aims at integrating data-driven optimisation and design of experiments techniques with model-based