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] Subject Areas: high-dimenstional statistics, Machine Learning theory, Mathematical foundations of AI Appl Deadline: none (posted 2026/03/06 05:00 AM UnitedKingdomTime) Position Description: Apply Position
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assay for high throughput analysis. Assess and validate sensor performance with real samples; ensure data reliability and reproducibility. Develop strategies to address matrix effects and potential
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neurodegenerative processes. Responsibilities: Conduct biochemical, genetic, and transcriptomic analyses of key regulators of brain aging using mouse and cerebral organoid models. Design and perform experiments
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performance. For additional information regarding the PI, associated research, and project institutions, see: http://gonetowar.com https://ghsm.hms.harvard.edu/person/faculty/joseph-gone http
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decarbonization, grid modernization, and the integration of distributed and flexible energy resources. Research topics may include—but are not limited to—AI-based grid operation and planning, reinforcement learning
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, and either flow cytometry or microscopy, or both. The ideal candidate values patience, curiosity, and hypothesis-driven science, and is eager to learn new model systems and/or techniques. High standards
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solver who wants to be part of a dynamic team. Information about the Church Lab: Learn more about the innovative work led by Dr. George Church here: https://churchlab.hms.harvard.edu/ , https
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of diverse measurement methods for detecting airborne infectious disease threats. A critical aspect of your role will be ensuring robust, sensitive sensor performance with real-world samples, and effectively
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: Learn more about the innovative work led by Dr. William Shih here: https://www.shih.hms.harvard.edu/ . What you’ll do: Design nucleic-acid nanostructures and assemble them in a wet laboratory
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about the Shih Lab: Learn more about the innovative work led by Dr. William Shih here: https://www.shih.hms.harvard.edu/ . What you’ll do: Develop DNA-based sensors that seed crisscross assembly of single