41 algorithm-"Multiple"-"U"-"Simons-Foundation"-"Prof"-"Prof"-"University-of-Wisconsin-Madison" Fellowship positions
Sort by
Refine Your Search
-
Country
-
Employer
- University of Bergen
- Nanyang Technological University
- University of British Columbia
- University of Oslo
- George Mason University
- Harvard University
- Johns Hopkins University
- Singapore Institute of Technology
- UNIVERSITY OF SOUTHAMPTON
- ;
- AbbVie
- Carnegie Mellon University
- Florida Atlantic University
- Genentech
- Integreat -Norwegian Centre for Knowledge-driven Machine Learning
- Marquette University
- Northeastern University
- University of Maryland, Baltimore
- University of South-Eastern Norway
- University of Waterloo
- 10 more »
- « less
-
Field
-
for Multi-Agent Decision-Making, https://oceanerc.com ). This timely project will develop statistical and algorithmic foundations for systems involving multiple incentive-driven learning and decision-making
-
Centre for Advanced Robotics Technology Innovation (CARTIN) is looking for a candidate to join them as a Research Fellow. Key Responsibilities: Develop novel algorithms for multi-agent inverse
-
conditions. Our work combines traditional statistical methods with advanced artificial intelligence algorithms to identify patterns in disease. We also use qualitative methods to understand lived experiences
-
. The successful candidate will play a pivotal role in a project centered around variational quantum algorithm in the near-term, especially on innovating advanced error mitigation or detection techniques to solve
-
algorithms, including machine unlearning techniques, to enhance model robustness and reliability. Design and execute rigorous AI testing frameworks to assess and mitigate risks in AI systems. Collaborate with
-
cooperative, competitive, and mixed settings. Collaborative decision-making frameworks and decentralized learning algorithms. Adaptive, meta-learning, and context-aware strategies to enhance policy
-
algorithms, including machine unlearning techniques, to enhance model robustness and reliability. Design and execute rigorous AI testing frameworks to assess and mitigate risks in AI systems. Collaborate with
-
conditions. Our work combines traditional statistical methods with advanced artificial intelligence algorithms to identify patterns in disease. We also use qualitative methods to understand lived experiences
-
and application of fast solvers for Maxwell’s equations and nonlinear inversion algorithms that we have already developed in a previous PhD project. In addition to electromagnetic geophysics
-
electromagnetic data during drilling. This includes the further development and application of fast solvers for Maxwell’s equations and nonlinear inversion algorithms that we have already developed in a previous