Sort by
Refine Your Search
-
Country
-
Employer
- NEW YORK UNIVERSITY ABU DHABI
- Eindhoven University of Technology (TU/e)
- KTH Royal Institute of Technology
- Aarhus University
- Argonne
- Carnegie Mellon University
- Iowa State University
- Singapore-MIT Alliance for Research and Technology
- Technical University of Munich
- University of Delaware
- University of Illinois at Chicago
- 1 more »
- « less
-
Field
-
Description Job description Game theory is rapidly gaining traction in several engineering applications as the natural framework for multi-agent decision making. Yet, unlike optimization, game theory has
-
cardiovascular monitoring and health assessment. 2, Develop multi-physics simulation frameworks for the design and optimization of ultrasonic transducer arrays and acoustic beamforming. Apply advanced optimization
-
projects focused on optimization, data analytics, and control of power distribution systems. This position offers the opportunity to collaborate closely with utility company partners and national
-
integration, finite-volume and finite-element methods, variational formulations, structure-preserving discretisations, optimal transport, and the numerical analysis of partial differential equations. The second
-
-VLMs, tiny-VLAs; Agentic-AI systems; and Robust Generative AI targeting hallucination, safety and security issues. Besides robustness, systems should also be optimized for high performance and energy
-
the nexus of AI, operations research and decision making and their applications to workforce policy and reskilling. The project builds on ongoing work using optimization-based models to predict future skill
-
developing optimization-driven approaches to multimodal device tailoring. We are looking for someone with A PhD in Human-Computer Interaction or a closely related field Strong programming skills (e.g., Python
-
-precision hardware without compromising simulation fidelity. Data-centric optimization: Designing and implementing communication schemes and data access strategies targeting post-exascale systems, e.g., using
-
device-relevant properties Design active learning, Bayesian optimization, uncertainty-aware modeling, and other adaptive experimental design workflows to guide experiments and improve data efficiency in
-
, structure-preserving discretisations, optimal transport, and the numerical analysis of partial differential equations. The second position will focus primarily on the geometric representation of complex data