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Field
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Position Summary This position will primarily support the implementation of a newly funded National Institute of Mental Health R01 grant focused on optimizing a digital chatbot intervention for
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machine learning—for chemical and biological applications. You will design and implement models ranging from molecular to process scales, develop model-predictive control and optimization strategies, run
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. Work with NSLS-II automation team and engineering support staff to develop the next generation of robotics and sample preparation protocols for XCFS. Optimize crystallization systems for XCFS campaigns
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are available. Appointments cannot exceed five years. KEY RESPONSIBILITIES: Synthesize and optimize CNS-targeted ligands and radiolabeled compounds. Develop PET tracers for neuroreceptors, enzymes
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Laboratory seeks a postdoctoral appointee to join a multidisciplinary team developing complex systems models, including agent-based models, and new algorithms and tools for machine learning and optimization
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development experience GTSAM or similar factor graph optimization frameworks Field robotics deployment in challenging environments Multi-sensor calibration and fusion Commitment to open-source development and
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related field in hand by the time of the appointment. Strong background in distributed control, optimization, or multi-agent systems. Proven track record of high-quality publications. Proficiency in
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culture of resilient optimism, resourceful thinking and pragmatic dreaming that enables us to reach others every day. Visit our website to learn more about the University at Buffalo. University at Buffalo
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from telescope data. The design of robust uncertainty quantification tools is a core component of this effort. -On the experiment design side, the group develops simulation and optimization algorithms
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optimal for search engines. PDF must not require special certificates or passwords to open. Max file size is 10MB. If you have questions, or have issues uploading documentation, please send an email to DOE