Sort by
Refine Your Search
-
Listed
-
Employer
- Northeastern University
- Harvard University
- Indiana University
- Cornell University
- University of Maryland, Baltimore
- Johns Hopkins University
- Nature Careers
- AbbVie
- Carnegie Mellon University
- Dana-Farber Cancer Institute
- SUNY University at Buffalo
- The University of Memphis
- University at Buffalo
- University of Colorado
- University of Idaho
- University of Michigan
- University of Michigan - Ann Arbor
- 7 more »
- « less
-
Field
-
development of a novel generative AI framework for structural biology. This project sits at the intersection of X-ray scattering and deep learning, aimed at integrating experimental data to predict protein
-
do things, especially considering recent advancements in AI technology. The position will include developing radiomics and deep learning models from contrast-enhanced computed tomography images
-
for structural biology. This project sits at the intersection of X-ray scattering and deep learning, aimed at integrating experimental data to predict protein ensemble structures. As an Empire AI-funded fellow
-
, multimodal, and agentic AI, as well as foundation models, with a focus on geometric deep learning, large-scale knowledge graphs, and large language models. Fellows will also have the opportunity to apply
-
. in Linguistics, Computer Science, Cognitive Science, or a related field by the start date Strong background in computational linguistics or deep learning Demonstrated interest in at least one of
-
: Expertise in machine learning, deep learning, natural language processing and other AI methods in health and life sciences datasets Expertise in advanced computational methods such as network analysis, graph
-
time-management skills are a must. Research grant writing experience and a publication track record are highly desired. Preferred Qualifications: Expertise in machine learning, deep learning, natural
-
; distributionally robust optimization; 2) Graph Neural Networks, Large Language Models (LLMs), and geometric deep learning; and 3) federated learning and privacy preserving computing. Basic Qualifications Candidates
-
, computer science, data science, or similar · Strong publication record in peer-reviewed conferences and/or journals · Experience applying machine learning methods (especially deep neural network approaches
-
, applying deep domain knowledge and advanced quantitative methods to inform critical development decisions. At Northeastern University, the Fellows will engage in scientific publication, conference