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Details Title Postdoctoral Fellow in Energy System Optimization and Digitization School Harvard John A. Paulson School of Engineering and Applied Sciences Department/Area Position Description
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Details Title Postdoctoral Fellow in Riemannian Optimization School Harvard John A. Paulson School of Engineering and Applied Sciences Department/Area Position Description A postdoctoral position is
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for understanding how AI-enabled control, optimization, and market design can support large-scale decarbonization, grid modernization, and the integration of distributed and flexible energy resources. Research topics
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procedures, and electrophysiology. The candidate is expected to operate independently within defined protocols and contribute to troubleshooting and optimization of experimental systems. Basic Qualifications
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contribute to troubleshooting and optimization of experimental systems. Basic Qualifications: Bachelor's or Master's degree in Bioengineering, Biomedical Engineering, Neuroscience, Physiology, or Electrical
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the project directors and collaborators to develop data-driven and economically grounded frameworks for understanding how AI-enabled control, optimization, and market design can support large-scale
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. Research areas include Representation Learning, Machine learning and Optimization on graphs and manifolds, as well as applications of geometric methods in the Sciences. This is a one-year position with
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statistical inference/optimization, and will have the chance to mentor both undergraduate and graduate students in these areas (as it relates to joint projects). Special Instructions Required application
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-accurate-portable-diagnostics/. What you’ll do: Design, fabricate, characterize, and optimize electrochemical biosensing technologies for real-time detection. Develop and implement novel surface chemistries
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magnetic response. Development of machine learning methods for exchange-correlation functionals. Current work in the group is focused on improvements and performance optimizations for the recently developed