Sort by
Refine Your Search
-
Listed
-
Employer
- Oak Ridge National Laboratory
- University of Washington
- Stanford University
- University of Minnesota
- University of North Carolina at Chapel Hill
- Cornell University
- National Aeronautics and Space Administration (NASA)
- Northeastern University
- Princeton University
- Rutgers University
- The University of North Carolina at Chapel Hill
- Argonne
- George Washington University
- Indiana University
- SUNY Polytechnic Institute
- The University of Arizona
- University of California Berkeley
- University of Houston
- University of Texas at Arlington
- University of Utah
- Washington University in St. Louis
- Argonne National Laboratory
- Axoniverse
- Baylor College of Medicine
- Broad Institute of MIT and Harvard
- Brookhaven Lab
- Brookhaven National Laboratory
- Caltech
- Dartmouth College
- DePaul University
- Episteme
- Georgia Institute of Technology
- Harvard University
- Loyola University
- Massachusetts Institute of Technology
- Missouri University of Science and Technology
- North Carolina A&T State University
- Northwestern University
- Texas A&M University
- University of California Merced
- University of California, Berkeley
- University of California, Merced
- University of Florida
- University of Oklahoma
- University of Texas at Dallas
- University of Virginia
- Zintellect
- 37 more »
- « less
-
Field
-
typologically diverse languages Creating self-supervised learning algorithms that can assess phonological development and speech complexity in children from birth through age 6, with applications to both typical
-
working under the supervision of Prof. Jaideep Vaidya (the PI and Director, I-DSLA) to develop and analyze privacy-preserving solutions for biomedical data research, implementing the developed algorithms
-
travel allowance and access to advanced computing resources. The MMD group is responsible for the design and development of numerical algorithms and analysis necessary for simulating and understanding
-
structure-preserving discretization algorithms (a refinement of finite-element analysis compatible with exact geometric, topological, and physical constraints) with artificial neural networks for achieving
-
heavy software development component. The successful candidate will perform research in the application of machine learning (ML) techniques to the finite element method (FEM) in the context of composites
-
Number: JR91414 Position Summary The postdoctoral fellow will develop artificial intelligence applications to support characterization of medical data with a focus on radiology image, radiology reports
-
. Essential Duties and Responsibilities Neuroimaging data collection and management Data analysis and model building Develop advanced deep learning and machine learning algorithms. Assist with organizing large
-
-doctoral Associate will develop algorithms and theory for machine learning methods, as well as implement and apply ML methods to problems in domains such as computational biology and neuroscience. This is a
-
for the design and development of numerical algorithms and analysis necessary for simulating and understanding complex, multi-scale systems. The group is part of the Mathematics in Computation (MiC) Section
-
, Weight, and Power) DAS system. - Develop calibration and processing pipelines for stable, low-noise operation. - Plan and run environmental tests (thermal/vacuum, vibration) and analogue field trials