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dynamical systems), epidemiological modelling, data analysis (statistics, machine learning). • in scientific programming (preferably Python, Matlab, R) Genuine interest in the analysis and modeling
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field of civil and commercial law) Very good written and spoken German Very good written and spoken English Excellent computer skills (MS-Office) Experience in working with databases High level of written
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, engineering, or a related field. Strong programming skills and experience in machine learning or statistical modelling are essential. Experience with healthcare data, algorithmic fairness, or deep learning
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or more of the following: software package development and maintenance in R; record linkage/entity resolution; data privacy techniques; large data processing and high performance computing; advanced causal
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: What are efficient machine learning strategies to identify large ensembles of nanoparticles in tomograms (i.e., to identify nanoparticles on irregular 2D surfaces in 3D space)? What are appropriate
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at the intersection of AI, NLP, and industrial applications. Contribute to the development of scalable and interpretable AI tools for real-world deployment. Qualifications: A PhD in Computer Science, Machine Learning
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solutions for large-scale scientific data models in federated learning environments. You will advance privacy-preserving machine learning by developing efficient techniques that maintain robust privacy
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projects that apply machine learning and advanced computational modeling to integrate multi-omics, clinical, and imaging data for biomarker discovery and mechanistic insights in AD. The position offers
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 12 hours ago
the development and implementation of machine learning models Special Physical/Mental Requirements Special Instructions For information on UNC Postdoctoral Benefits and Services click here Quick Link https
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on developing machine-learning surrogates for electronic structure and electrostatic potential and using these models to predict structural and electronic evolution under applied bias. Methods may include density