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The successful candidate will develop generative machine-learning methods for amorphous molecular thin films — the supramolecular structures that govern the performance of organic-electronic materials
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countries (HIC). The airm of this PhD project is to understand how E. coli colonization contributes tot he risk of diarrheal disease and infections with AMR and use this knowledge to design interventions
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. Ideal candidates will have demonstrably strong research skills, evidenced by multiple publications in top-tier machine learning or artificial intelligence conferences and/or leading scientific journals
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for University Housing in the University Housing Department. How to Apply: Interested candidates may apply online at https://jobs.coastal.edu/postings/122679 Applicants must submit a cover letter, resume and list
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programs Posting Summary We are seeking qualified adjunct clinical instructors to teach on a per diem basis in our continuing education program through the Center for Professional Development. We deliver a
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engages substantially with artificial intelligence, data science, or scientific machine learning. The successful candidate will have a primary appointment in Mathematics and opportunities to collaborate
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the RISE 100 initiative, aiming to recruit 100 new tenure-system faculty to strengthen its research enterprise and leadership in key academic areas. Learn more at https://www.uta.edu/administration/president
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University of North Carolina Wilmington | Wilmington, North Carolina | United States | about 18 hours ago
cleaning, denoising, and prediction, including approaches based on statistical machine learning, deep learning, and Transformer architectures. Perform signal analysis in both the Fourier (frequency) domain
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, the following position is available from 1st January 2027: PhD Position in Learning Analytics and Self-Regulated Learning (m/f/d, E13 TV-L, 75%) DFG-funded project TRACE: Trajectories of Adaptation, Disengagement
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-facing web GIS dashboard. Investigate the forest, landscape, and climate conditions that drive storm susceptibility, using major windstorms as natural experiments and interpretable machine-/deep-learning