83 algorithms-"EPFL"-"INSAIT---The-Institute-for-Computer-Science" positions at Nature Careers
Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
literature. Foundational Algorithms for AGI AGI will not emerge from scaling existing models alone; it requires a new algorithmic foundation for learning, reasoning, and adaptation. This research area is
-
suitable data models [CSC+23]. Objectives As far as the design of efficient numerical algorithms in an off-the-grid setting is concerned, the problem is challenging, since the optimization is defined in
-
. Building on its strong foundation in Optics and Photonics, EECS is expanding its research into quantum algorithms and their industrial applications. These new initiatives will be integrated into a state-wide
-
About the Role We are seeking brilliant and passionate Algorithm Researchers to join our core team dedicated to advancing the frontiers of Artificial General Intelligence (AGI). In this role, you
-
efficient decoding algorithms" supported by the Luxembourg National Research Fund (FNR). The APSIA Group is seeking a highly qualified post-doctoral researcher for this project. For further information, you
-
minimizing error and maximizing efficiency, is computationally challenging—no known polynomial-time algorithm exists to solve it optimally in all cases. Because of this complexity, researchers typically rely
-
techniques and the structure of bilevel problems in large-scale settings. Objectives The goal of this postdoctoral project is to develop scalable blackbox optimization algorithms tailored to bilevel problems
-
programming of algorithms. The use of programming languages such as Python, R, SQL, and C++ will be a daily part of the project, and proficiency in these languages is required. However, additional datasets will
-
on stochastic Riemannian optimization algorithms, these methods still suffer from limitations in computational complexity. The post-doctoral fellow will build upon this preliminary work to investigate
-
algorithms for dynamic structured data, with a particular focus on time sequences of graphs, graph signals, and time sequences on groups and manifolds. Special emphasis will be placed on non-parametric