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both on the sequence and structural level, developing and employing machine-learning tools for predicting antibody-epitope binding. In silico antibody design is a long-standing computational and
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computational biology techniques to perform antibody design both on the sequence and structural level, developing and employing machine-learning tools for predicting antibody-epitope binding. In silico antibody
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both on the sequence and structural level, developing and employing machine-learning tools for predicting antibody-epitope binding. In silico antibody design is a long-standing computational and
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to support the candidate’s career development and will be planned in alignment with their skills and goals, where possible. Learn more about career support and career-enhancing work available at NHM
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challenges. It fosters collaborations and develops advanced computational tools through a hub for multi-omics and systems biology. Project description The PhD project aims to explore how multiple layers
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description The PhD project aims to explore how multiple layers of gene expression regulation—including DNA packaging, transcription initiation, and translation—interact to control gene activity. Using
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experience with building Docker containers. Experience with high-throughput sequencing data analysis (e.g., CAGE, ATAC-seq, ChIP-seq, or Hi-C). Familiarity with epigenetics, gene regulation, or chromatin
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experience with building Docker containers. Experience with high-throughput sequencing data analysis (e.g., CAGE, ATAC-seq, ChIP-seq, or Hi-C). Familiarity with epigenetics, gene regulation, or chromatin
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isolation, microscopy, proteomics, single cell RNA-sequencing, bioinformatics, and/or human stem cells will be viewed as an asset Willing to work with animal models Fluent oral and written communication
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, dynamic, and heterogeneous data integration problem, where challenges include missing or noisy data, redundancy across sources, dynamic updates, and the need for robust semantic alignment and