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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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with other group members, collecting amino acid sequence data from across multiple fully sequenced genomes. The student will conduct a variety of measures of protein phenotypic diversity, in the form
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-throughput sequencing of genomes and biomes, continuous recording of video and audio in the wild, high-throughput imaging of biological specimens, and large-scale remote monitoring of organisms or habitats
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components of organisms’ general life-history decisions. The work involves accumulation of mutations in different fly lines, extracting DNA and building libraries for sequencing, as well as bioinformatics
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data streams offered by techniques such as high-throughput sequencing of genomes and biomes, continuous recording of video and audio in the wild, high-throughput imaging of biological specimens, and
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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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of cancer cells. The models are trained on high-throughput datasets, including metabolomics, proteomics, and transcriptomics, and constrained to align with the cell’s molecular networks. This allows us to
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of Stockholm Trio (KI, KTH, SU) and is the only SciLifeLab site that co-localizes researchers from multiple universities under one roof. By working together Stockholm Trio has been able achieve a significant