Sort by
Refine Your Search
-
Listed
-
Country
-
Employer
- Nanyang Technological University
- University of Bergen
- Harvard University
- Singapore Institute of Technology
- University of British Columbia
- University of Michigan
- ;
- Carnegie Mellon University
- Dana-Farber Cancer Institute
- FCiências.ID
- Florida Atlantic University
- George Mason University
- Johns Hopkins University
- Marquette University
- Massachusetts Institute of Technology
- Northeastern University
- UNIVERSITY OF SOUTHAMPTON
- University of Maryland, Baltimore
- University of Michigan - Ann Arbor
- University of Oslo
- University of Sydney
- University of Waterloo
- Western Norway University of Applied Sciences
- 13 more »
- « less
-
Field
-
of recommendation algorithms based on multiple data related to microorganisms and pathogens, and the implementation of the recommendation system on a testable platform. The work also includes the writing of technical
-
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
-
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
-
algorithm that reliably simulates two-phase flow. The PhD projects will be part of developing and analyzing relevant numerical methods and implement them in an open-source framework that will be made openly
-
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
-
dialogue on AI governance. Manage multiple projects simultaneously, ensuring timelines are met and resources are effectively allocated. Collaboration & Knowledge Exchange Collaborate with policymakers
-
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
-
particular sequential, multiple assignment, and randomized trial design. You will have the opportunity to mentor the PhD students on the team. Experience in any of the following areas may be useful: SMART