Sort by
Refine Your Search
-
Employer
- Harvard University
- AbbVie
- The University of Alabama
- University of Michigan
- Indiana University
- Nature Careers
- Northeastern University
- Johns Hopkins University
- University of Alabama, Tuscaloosa
- Zintellect
- Binghamton University
- Genentech
- Simons Foundation
- Stanford University
- University of Cincinnati
- University of Rhode Island
- University of South Carolina
- University of Texas at Austin
- American University
- Arizona State University
- Auburn University
- Carnegie Mellon University
- Central Michigan University
- City of Hope
- Colorado State University
- Cornell University
- Duke University
- Emory University
- George Mason University
- James Madison University
- Marquette University
- Montana State University
- National Renewable Energy Laboratory NREL
- SUNY University at Buffalo
- Saint Louis University
- University of Kansas Medical Center
- University of Maryland
- University of Michigan - Ann Arbor
- University of North Carolina at Chapel Hill
- University of North Carolina at Charlotte
- 30 more »
- « less
-
Field
-
Computer Science, Biomedical Engineering, Pathology Informatics, or a related field, with emphasis on computer vision and machine learning (summer and fall graduates are also welcome to apply) Proficiency in
-
a track record in computational modelling that explores the dynamics of AI systems and the development of autonomous AI agents, experience with machine learning, reinforcement learning, and generative
-
with machine learning techniques for robotic decision-making and intelligent control for tasks with high uncertainties. Experience with research on multi-agent collaboration and decentralized control
-
robust professional development opportunities, and a competitive benefits package designed to support your career and well-being. Learn about NREL’s critical objectives: NREL's Mission and Vision . Job
-
, computational, and machine learning/AI methods, with a particular emphasis on deep learning approaches improve our understanding and prediction of infectious disease dynamics. Projects are also strongly grounded
-
research is open; we are particularly interested in those working in the period between 1900 and the present. Open to scholars with PhDs in Science and Technology Studies and related fields whose research
-
use of molecular assays such as real-time PCR and next-generation sequencing to study the prevalence of infectious diseases in the target study populations. Learning about sample testing under a
-
strong, demonstrated interest to conduct academic research in a relevant field Interest in legal research Interest in causal inference and social science Experience with machine learning / deep learning
-
condition leading to medical discharge following combat related trauma in our military. Learning opportunities include, but are not limited to: exposure to various aspects of pre-clinical research by
-
neuroimaging and fluid biomarkers, (b) systems biology analysis of pathways from multi-omics data using multi-layered network approaches, © machine learning for identification of genetic risk factors in ADRD, (d