Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
) signal processing, machine/deep-learning and computational linguistics. The team mobilizes them to produce methodologically sound research in response to some of the challenges posed by the nature and
-
methods so that design decisions can be understood, validated, and trusted. As a postdoc, you will: Develop generative AI models (e.g., variational autoencoders, diffusion models, or reinforcement learning
-
an excellent environment for deep innovation, out-of-the-box thinking, and creative problem solving. We will teach you what you do not yet know through mentoring, peer support, and many educational opportunities
-
and provide relevant information to inform NIST management and stakeholders - Maintain deep technical knowledge of advances in the application of artificial intelligence and machine learning
-
members have been working on statistics learning, granular computing and knowledge discovery, machine learning, deep learning, and specifically interpretable artificial intelligence. Many innovative
-
Postdoctoral position in the development of an AI-based phenotyping system for high-throughput sc...
work. Qualifications PhD in computer science, computational biology, engineering, or related fields. Experience developing deep-learning tools for image processing, automatic monitoring of agricultural
-
environment where machine learning meets real-world scientific impact. What You’ll Do: Conduct cutting-edge research at the intersection of AI and science Develop large-scale deep learning models for scientific
-
work. Qualifications PhD in computer science, computational biology, engineering, or related fields. Experience developing deep-learning tools for image processing, automatic monitoring of agricultural
-
. Experience in high-throughput sequencing data analysis and cluster/cloud computing. Proficiency in variant calling, single-cell DNA and/or RNA analysis, and machine/deep learning (preferred but not required
-
computer vision tools (e.g., MediaPipe, OpenPose, homography estimation, optical flow). Experience with eye-tracking data collection or analysis. Familiarity with deep learning frameworks (PyTorch