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Field
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on genetic hearing loss in the Molecular Otolaryngology and Renal Research Laboratories (MORL) at the University of Iowa. Hearing research in the MORL is broadly focused on Mendelian forms of genetic hearing
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to conduct research on developing and using machine learned parameterizations developed from ocean-data assimilation increments. The goal is to develop parameterizations of unresolved processes that will
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literal contributions to the scientific community. CORE JOB FUNCTIONS Develops and implements advanced computational and machine learning methods for the analysis of large-scale omics data, including
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the statistics of tropical cyclones over the past millennium, recent historical era and the coming centuries, using a combination of high-resolution climate modeling, machine learning/artificial intelligence (ML
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to funding. This post is full time. This project will involve adapting and using our existing ultracold erbium and potassium machine for studies of e.g. turbulence, dipolar supersolids and polarons. We
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incident conditions, i.e., mainly higher incidence energies, which requires the development and implementation of a new machine-learning model to represent the tensorial electronic friction coefficients
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bioinformatics Experience with applying AI (deep learning, probabilistic modelling, generative AI) or machine learning in the field of systems biology Proficient in Python or R programming Strong communication
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their doctoral degree prior to that may also be eligible. Special reasons include absence due to illness, parental leave, appointments of trust in trade union organisations, military service, or similar
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applied to amorphous materials. You have familiarity with artificial intelligence (AI) and machine learning (ML) methodologies and interested in advancing these tools for accelerating the analysis
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-level understanding of kinetics in solid-solid phase transitions; Develop advanced machine learning methods for the fast prediction of materials properties; Publish results in peer-reviewed journals and