Sort by
Refine Your Search
-
Listed
-
Employer
- Northeastern University
- Harvard University
- Cornell University
- Dana-Farber Cancer Institute
- Indiana University
- Nature Careers
- University of Colorado
- University of Maryland, Baltimore
- University of Michigan
- AbbVie
- Carnegie Mellon University
- The University of Memphis
- University of Alabama, Tuscaloosa
- University of Cincinnati
- University of Idaho
- 5 more »
- « less
-
Field
-
; 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
-
Informatics (DBMI) at Harvard Medical School and the Yu Lab are seeking a Postdoctoral Research Fellow with experience in machine learning and scientific programming. The candidate will work with a multi
-
Informatics, Health Data Science, Biostatistics, or a closely related area. Strong ML/deep learning foundation plus expertise in at least one of: multimodal learning, time-series modeling, or NLP. Demonstrated
-
the start date Strong background in computational linguistics or deep learning Demonstrated interest in at least one of: language learning/acquisition, interpretability/mechanistic analysis, human-like
-
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
-
opportunity to field test and validate their methods using real-world systems. Postdoctoral fellows will work across the following research areas: Predictive machine learning Robust and stochastic optimization
-
School: Harvard Medical School Position Description: The Department of Health Care Policy at Harvard Medical School seeks an experienced postdoctoral fellow with a specialty in health services research
-
, Social Sciences , Biomedical Informatics , Causal Inference , Computational Social Science , Data Science and Information , Data Visualization , Deep Learning , High dimensional Data , Large Language
-
Science, Biostatistics, or a closely related area. Strong ML/deep learning foundation plus expertise in at least one of: multimodal learning, time-series modeling, or NLP. Demonstrated working experience
-
, and the ability to read related scientific papers on cancer combination therapy. It would also require expertise in relevant AI methodology, such as deep learning architectures for property prediction