12 electrical-engineering-"https:"-"Embry-Riddle-Aeronautical-University" research jobs at Harvard University
Sort by
Refine Your Search
-
Listed
-
Program
-
Field
-
system transformation. Basic Qualifications: Ph.D. in electrical engineering, applied mathematics, economics, operations research or a related field. Additional Qualifications: Candidates who have a strong
-
scientists, engineers, and/or doctors! The lab is committed to fostering lifelong learners in an environment that is diverse, inclusive and respectful. Learn more about our lab here: https
-
Details Title Postdoctoral Fellowship in Power and AI Systems School Harvard John A. Paulson School of Engineering and Applied Sciences Department/Area Computer Science/ Electrical Engineering
-
Details Title Postdoctoral Fellow in Geometric Machine Learning School Harvard John A. Paulson School of Engineering and Applied Sciences Department/Area Applied Math Position Description A
-
Expected Start Date: July – Sep 2025 Basic Qualifications A PhD in Bioengineering, Biomedical Engineering, Electrical Engineering, Mechanical Engineering, and/or Neuroscience Extensive experience with
-
Details Title Postdoctoral Fellowship in Reinforcement Learning, Probabilistic Methods, and/or Interpretability School Harvard John A. Paulson School of Engineering and Applied Sciences Department
-
solver who wants to be part of a dynamic team. Learn more about the innovative work led by Dr. Don Ingber here: https://wyss.harvard.edu/technology/erapid-multiplexed-electrochemical-sensors-for-fast
-
team. Learn more about the innovative work led by Dr. Don Ingber here: https://wyss.harvard.edu/technology/erapid-multiplexed-electrochemical-sensors-for-fast-accurate-portable-diagnostics/. What you’ll
-
inaccessible regimes. Work in the lab combines optics, protein engineering, chemistry, electrophysiology, simulation, and theory. We work at the levels of individual molecules, single cells, and whole
-
recordings, behavioral training, and visual experimentation, while also developing and testing deep neural network models of visual representation. In short: experiments first, models second. Current and