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will use machine learning methods to develop affinity ligands. These methods have been transformative for protein design, allowing generation of novel proteins which can suit a precise need. In this 4
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and functional diagnostic methods. These methods are used in everything from experimental research to clinical studies on patients and large-scale epidemiological studies on volunteers. In
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information about us, please visit: the Department of Biochemistry and Biophysics . About the DDLS PhD student program Data-driven life science (DDLS) uses data, computational methods and artificial
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experience working with deep learning methods for biomedical applications. Academic and Project Excellence: Evidence of high-quality research work, as demonstrated by academic grades, the merit of prior degree
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spectrum, in topics in virology and immunology, and currently specializes in computational biology focusing on developing methods and applications of deep learning for protein sequence and structure, as
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methods based on optimal transport for addressing problems in signal processing, control theory, and inverse problems. The doctoral student project and the duties of the doctoral student By developing novel
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, computational methods and artificial intelligence to study biological systems and processes at all levels, from molecular structures and cellular processes to human health and global ecosystems. The SciLifeLab
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, computational methods and artificial intelligence to study biological systems and processes at all levels, from molecular structures and cellular processes to human health and global ecosystems. The SciLifeLab
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) program. Data-driven life science (DDLS) uses data, computational methods and artificial intelligence to study biological systems and processes at all levels, from molecular structures and cellular
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) programme and research school Data-driven life science (DDLS) uses data, computational methods and artificial intelligence to study biological systems and processes at all levels, from molecular structures