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, evolution, and machine learning, which is part of HHMI’s AI for Science Initiative (ai.hhmi.org ). Our goal is to integrate evolutionary biology principles into protein language models. This role will
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, especially viruses and bacteria evolving within their hosts and solid tumors progressing to malignancy. In particular, they are characterizing how spatial organization shapes these evolutionary outcomes and
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wide range of approaches: protein biochemistry and reconstitution, single-molecule biophysics, proteomics, metabolomics, fungal genetics, evolutionary analysis, mammalian cell culture, and live-cell
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software packages. Familiarity with core data structures, algorithms, and some exposure to parallel or distributed computing. Strong quantitative and critical-thinking abilities demonstrated through
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more recently discovered phage-defense systems that have not yet been studied in detail – with the goal of better understanding the evolutionary significance of the diversity of the prokaryotic immune
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data to generate mathematical models of cellular signaling dynamics. You will help design algorithms for data-driven model discovery, test proposed algorithms in computational simulations, and
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and detail-oriented Research Technician(s) to join our team. Our lab investigates bacteria–phage conflicts, evolutionary arms races, and bacterial defense systems. These host–pathogen battles have
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Malik in the Basic Sciences Division of the Fred Hutchinson Cancer Center in Seattle. The Malik lab focuses on conducting evolutionary studies of genetic conflict to gain insight into their mechanisms and
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implement algorithms for machine vision, adaptive control, and real-time learning to support fully autonomous experimentation. Document and share new designs, workflows, and analytical tools with the broader
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approach to examine odor detection and perception in insects, from the molecular basis for odorant detection to the neural and behavioral algorithms underlying olfactory plume navigation. One focus