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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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of MSI advances our understanding of complex brain processes. The prospective PhD candidate collects brain MSI data and develops novel machine learning methods in connection to generative models such as
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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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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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variability in risk factor susceptibility, treatment response, disease pathogenesis, and clinical diagnosis (biostatistics, machine/deep learning), ii) Investigating causal processes and disease mechanisms
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university. More information about us, please visit: the Department of Biochemistry and Biophysics . Project description Project title: Biology-informed Robust AI Methods for Inferring Complex Gene Regulatory
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project: Computational methods for complex SV detection using sequencing data Main supervisor: Kristoffer Sahlin, ksahlin@math.su.se . Co-supervisor: Adam Ameur, adam.ameur@igp.uu.se . In the Department
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Uppsala University, Department of Information Technology Are you interested in developing new image analysis and machine learning methods for precision medicine and clinical decision support? Would
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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