26 evolutionary-algorithm Postdoctoral research jobs at NEW YORK UNIVERSITY ABU DHABI
Sort by
Refine Your Search
-
learning, and AI-driven manipulation. This position offers the opportunity to work on real-world robotic systems and develop novel algorithms at the intersection of robot learning, control, and AI
-
the development of mathematical models for signal transmission and reception, derivation of fundamental performance limits, algorithmic-level system design, and performance evaluation through computer simulations
-
models for signal transmission and reception, derivation of fundamental performance limits, algorithmic-level system design, and performance evaluation through computer simulations and/or experimental
-
applications for a Post-Doctoral Associate position, in the area of Quantum Algorithms. The candidate is expected to conduct research in computer science focusing on the combinatorial aspects of quantum
-
will help design and build novel systems and algorithms that challenge traditional assumptions in databases, guided by both real-world needs and formal foundations. They will work at the intersection
-
working at the intersection of machine learning, algorithmic fairness, human-computer interaction, and responsible AI. The project aims to investigate how bias emerges in data pipelines and AI systems
-
, or the design of efficient, explainable, and scalable query engines. The successful applicant will help design and build novel systems and algorithms that challenge traditional assumptions in databases, guided by
-
to contribute to cutting-edge research in robot intelligence, machine learning, and AI-driven manipulation. This position offers the opportunity to work on real-world robotic systems and develop novel algorithms
-
the development of mathematical models for signal transmission and reception, derivation of fundamental performance limits, algorithmic-level system design, and performance evaluation through computer simulations
-
research team working at the intersection of machine learning, algorithmic fairness, human-computer interaction, and responsible AI. The project aims to investigate how bias emerges in data pipelines and AI