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laboratory Our research focuses on large-scale pan-cancer genomics to gain insight into the genes, mutational processes and evolution of cancer. Our work is highly data-driven, with a focus on large-scale data
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successes, ARL civilian employees helped develop the proximity fuze, worked to develop ENIAC (Electronic Numerical Integrator and Computer, the first operational, general purpose, electronic digital computer
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foundation in computational or statistical genetics. Experience analyzing large-scale datasets is essential, as the work will involve complex genomic and multi-omics data. Familiarity with machine learning
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bioinformatics, computational biology, genomics, statistical genetics, or a related quantitative field, together with demonstrated expertise in large-scale genomic data analysis and significant experience in
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of this programme. The profile PhD in computer vision, computational biology, physics or a related discipline Demonstrated expertise in image analysis and working with large-scale imaging datasets Strong expertise in
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. The Fellow will work in close partnership with the lab's experimental team to build and apply analytical frameworks that translate these data into mechanistic insight and therapeutic hypotheses. As part of
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, innovation and exploration across many disciplines. A space where the brightest minds can pursue big and bold ideas and discover answers to crucial scientific questions. We support them in a dynamic
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pursue big and bold ideas and discover answers to crucial scientific questions. We support them in a dynamic environment which fosters excellence with state-of-the-art infrastructure, cutting-edge
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supercomputer for large-scale simulation and adversarial testing The project also includes industrial validation with social robotics platforms (e.g., QTrobot) for deployment in educational and special-needs
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, in vitro models, and large collections of well-annotated clinical specimens. We employ state-of-the-art computational biology/bioinformatics approaches to dissect acute and adaptive responses to RAS