Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Field
-
the Interpretable Machine Learning Lab (https://users.cs.duke.edu/~cynthia/home.html ) for a scientific developer to work in collaboration with other researchers on machine learning tools that help humans make better
-
University of California, Berkeley, Department of Electrical Engineering and Computer Sciences Position ID: University of California, Berkeley -Department of Electrical Engineering and Computer
-
, United States of America [map ] Subject Areas: Statistics, Machine Learning, and AI Appl Deadline: 2025/07/01 11:59PM (posted 2024/10/31, listed until 2025/07/01) Position Description: Position Description The University
-
area, with content covering robotics and machine learning, and excellent programming skills in Python. You should have research experience in either robotics or machine learning. You should also have
-
The Machine Learning section of the Department of Computer Science at the Faculty of Science at the University of Copenhagen (DIKU) is offering a 2 and a half year fully-funded postdoctoral position
-
papers with the other team members. What we are looking for: You must have a master’s degree in a relevant area, with content covering robotics and machine learning, and excellent programming skills in
-
spanning multiple diseases. About the lab: The Glastonbury Lab is focused on developing and applying Machine Learning to problems in digital pathology and spatial transcriptomics. The group has a particular
-
-of-computing-science/ Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data-driven models for complex data, including high
-
will be tailored to your expertise, spanning from hardware design to system-level optimization and control methods. For the AI position, you will develop machine learning models that incorporate physical
-
research focuses on a geometric understanding of training in deep neural networks. The position offers excellent training opportunities at the intersection of machine learning and applied mathematics