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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
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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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of the art (self-supervised) computer vision algorithms (e.g., DINO, Masked Autoencoders, SAM). Experience with ML model deployment, workflow orchestration, and high-throughput data processing and model
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develop algorithms that align image level embeddings across modalities (e.g., fluorescence ↔ electron microscopy ↔ brightfield ↔ …). In collaboration with other engineers and scientists, you will use
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is to discover governing equations from experimental data to generate mathematical models of cellular signaling dynamics. You will help design algorithms for data-driven model discovery, test proposed