Sort by
Refine Your Search
-
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
-
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
-
countervailing immunoediting processes that seek to control and eradicate these cancers. This project specifically focuses on ovarian cancer, a difficult-to-treat and life-threatening cancer for which early
-
scalable bioinformatics pipelines on cloud-based infrastructure. The Research Fellow will be responsible for the code base supporting the large-scale genomic processing and analysis pipelines at the SMaHT
-
command of data wrangling, cleaning, and large-scale dataset management. Machine Learning/Deep Learning: Experience with PyTorch, TensorFlow, or Hugging Face; embedding models; and model validation
-
, Electrical Engineering, Mechanical Engineering, and/or Neuroscience Extensive experience with at least two of the following: electrophysiology, rodent experimentation, programming of controllers
-
at least two of the following: electrophysiology, rodent experimentation, programming of controllers, neuroprosthetics or neuromodulators, peripheral nerve surgery, neural interfacing models Track record
-
integrates three synergistic research thrusts: Advanced thermal storage and sensing materials Intelligent, building-scale integration of sensing and control systems Cooperative, urban-scale energy management
-
treatment-control interference; · Programming/scripting knowledge suitable for processing raw data for analysis (e.g., text manipulation); · One or more computational environments for statistical analysis
-
strong command of data wrangling, cleaning, and large-scale dataset management. Machine Learning/Deep Learning: Experience with PyTorch, TensorFlow, or Hugging Face; embedding models; and model validation