Sort by
Refine Your Search
-
Listed
-
Country
-
Employer
- University of Bergen
- Nanyang Technological University
- Northeastern University
- Harvard University
- University of British Columbia
- University of Oslo
- George Mason University
- Johns Hopkins University
- Singapore Institute of Technology
- AbbVie
- Carnegie Mellon University
- Florida Atlantic University
- Genentech
- Integreat -Norwegian Centre for Knowledge-driven Machine Learning
- MOHAMMED VI POLYTECHNIC UNIVERSITY
- Marquette University
- National University of Singapore
- UNIVERSITY OF SOUTHAMPTON
- University of Maryland, Baltimore
- University of Michigan
- University of South-Eastern Norway
- University of Texas at Austin
- University of Waterloo
- Western Norway University of Applied Sciences
- 14 more »
- « less
-
Field
-
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
-
cooperative, competitive, and mixed settings. Collaborative decision-making frameworks and decentralized learning algorithms. Adaptive, meta-learning, and context-aware strategies to enhance policy
-
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
-
problem-solving skills, with a focus on developing innovative solutions for multi-modal LLMs. Self-motivated and able to work independently, managing multiple tasks and projects in a fast-paced environment
-
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
-
Innovation (UKRI), focusing on populations with multiple long-term conditions. You will contribute to a social care initiative, developing and testing an AI-informed digital tool to help individuals with
-
designs and methods, clinical trial methods, Bayesian methods, and developing R packages and scalable algorithms. Opportunities for collaboration across the Department of Biostatistics and the Medical
-
inferences, but are also developed in a manner that garners public confidence in their applications. To achieve this confidence, we need to prove that the conclusions drawn from machine learning algorithms
-
research initiatives that analyze the impact of AI technologies on social systems and design innovative methods for adapting economic and social governance to AI development. A special focus will be
-
project bridges two foundational fields in computer science and mathematics: Theory of Algorithms and Extremal Combinatorics. By integrating these areas, the project seeks to develop innovative