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Scientist to join the Aso Lab at the Janelia Research Campus. The Aso Lab is a team of interdisciplinary scientists investigating the mechanisms of learning by studying the neural circuits of the mushroom
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adept at computer programming, has a strong command of statistical data analysis and data visualization, who will work as a Computational Biologist. As a Ph.D. Data Scientist, you will join a dynamic team
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-paced, highly collaborative environment. It is ideal for individuals planning to spend two years gaining hands-on research experience prior to pursuing a PhD or graduate program, offering exceptional
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, machine vision, AI/ML systems, or autonomous control to join our collaborative neuroengineering efforts. This person will build AI-driven robotic systems for high-throughput brain imaging and behavioral
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microscopy, and machine learning. Our goal is to develop a protein biosensor optimization pipeline that integrates high-throughput functional screening with predictive deep learning to accelerate the directed
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ferroptosis, metabolism, and inflammation. What You Bring PhD in immunology, metabolism, regeneration, or cell biology (0–3 years post-PhD preferred). Proficiency in in vivo models, immune–metabolic signaling
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understanding how moving bodies and perception are integrated, we can develop more effective clinical interventions and inspire the next revolution in AI - one that reflects how biological brains learn through
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the brain support flexible behavior and strategy-switching? Learn more about the Dennis lab here What we provide: A collaborative lab environment with strong support for your research and development. Access
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coordination and environmental plasticity. The team member will be an integral member of our team whose responsibilities will be to provide general lab support to the team (50%) and to work directly with PhD
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of 3 years post-PhD experience (academic, core facility, or industry). Minimum of 3 years of experience working in the areas of image acquisition, image processing, and computer-assisted image analysis