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brain organoids. This position requires a high-degree of team work and interdisciplinary activities. What You Will Do: Perform research in machine learning methods based on control theory applied
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implementing bioinformatics pipelines from raw data, applying a range of advanced statistical, machine learning, and artificial intelligence (AI) techniques to analyse 'omics and clinical data, and contributing
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novel research methodologies in computer vision, deep learning architectures, and neuro-fuzzy systems to contribute to the development of robust AI frameworks for medical diagnosis and treatment support
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without prior experience will be considered if willing to learn. Interest in tumor metabolism and a strong desire to gain experience in bulk and spatial metabolomics. Solid bioinformatics skills, including
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large scale flow network simulations, machine learning, and methods from topological data analysis, to a broad set of problems. Examples include modeling vertebrate and invertebrate circulatory systems
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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 8 hours ago
& Computer Engineering Work Location EPIC Vacancy Open To All Candidates Position Designation Post Doc Employment Type Temporary - Part-time Hours per week 40 Work Schedule Pay Rate $48,000 - $53,000 Minimum
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Chemistry o Combinatorics, Algorithm, Extremal Graph Theory, Computing Theory o Programming Language, AI Theory or Machine Learning o Classical and Quantum Algorithm for Computational Quantum Many-body Theory
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of Birmingham is inviting applications for a Research Fellow position focused on Machine Learning for Automated Formal Verification. Machine learning has transformed programming, with code generation rapidly
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engineering and a strong foundation in data science. You bring a passion for solving complex problems and a track record of research excellence in optoelectronic materials, machine learning, or related fields
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superlattices (twistronics). The role will focus on developing and applying theoretical models and computational quantum chemistry and machine learning methods to uncover novel properties and phenomena in low