Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
-
Field
-
to demonstrate real-world feasibility. The overarching goal is to bridge high-level algorithmic innovation with energy-aware hardware deployment, enabling intelligent sensor systems that act as autonomous micro
-
implementation of the developed models into simulation codes and algorithms. Work closely with project partners from other leading research institutions. Present research findings at international conferences and
-
sciences, who ad- dress key issues in AI such as reproducibility, safety, trustworthiness and robustness, and who engage with the theoretical and algorithmic foundations of AI. A strong commitment to
-
language processing, algorithmic learning, fault-tolerance, blockchains, consensus, cryptocurrencies, digital money, central bank digital currency, decentralized finance, financial networks, e-democracy, voting, social
-
technologies (i.e. computing, data, algorithms, AI) in society. We are in particular looking for candidates who have interest and experience with STS and humanities pedagogy in the context of a technical
-
are particularly interested in candidates working on quantum error correction, algorithms, simulation and qubit architectures. We are also interested in outstanding scientists in theoretical quantum computing in a
-
collaboration with the Intelligent Maintenance and Operations Systems (IMOS) Laboratory at EPFL (Prof. Olga Fink). IMOS focuses on the development of intelligent algorithms designed to improve the performance
-
has been created in theoretical quantum computing. We are particularly interested in candidates working on quantum error correction, algorithms, simulation and qubit architectures. We are also
-
reinforcement learning for large language models (LLMs). Research directions include developing next-generation post-training algorithms, exploring diffusion-based approaches to reasoning with language models
-
interdisciplinary work, for example in medicine or life sciences, who address key issues in AI such as reproducibility, safety, trustworthiness and robustness, and who engage with the theoretical and algorithmic