-
large‑scale systems neuroscience with machine learning to derive principled models of cortical computation. Our newly refurbished primate facility, state‑of‑the‑art Neuropixels rigs, and high
-
, machine learning, computer science, statistics, engineering, medicine, or a related field. Strong candidates may have experience with large-scale human datasets, machine learning, statistical genetics
-
technical expertise in machine learning, artificial intelligence, and data modeling approaches. We are committed to investigating the mechanisms of brain aging and developing innovative interventions to
-
integrates single-cell and single-nucleus multi-omics, machine learning/AI, computational biology, and experimental validation to understand cardiovascular disease progression and identify novel therapeutic
-
Affairs. The FY27 minimum is $79, 056. Our research team is seeking a postdoctoral scholar to contribute to our research program focused on clinically applied machine learning, foundation models and multi
-
UC Berkeley, Bakar Institute of Digital Materials for the Planet | Berkeley, California | United States | about 20 hours ago
Job Details At BIDMaP (Bakar Institute of Digital Materials for the Planet) UC Berkeley, researchers are combining AI, machine learning, and data science with the physical sciences to confront climate
-
cardiac surgery outcomes research and predictive analytics using advanced statistical and machine learning methods applied to large claims and electronic health record databases and multimodal data
-
density in regulating these processes. The insights gained from these empirical studies will be paired with computational biology and machine learning to develop predictive AI models of how cells
-
machine learning, reduced-order modeling, or data-driven modeling of physical systems. You will conduct research on the development of fast, trustworthy, and data-efficient surrogate models that
-
detection, waveform segmentation, feature extraction, hemodynamic modeling, time-series analysis, machine learning, and development of algorithms for real-world clinical data. The fellow will have the