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11:59PM (posted 2025/06/30, listed until 2025/10/21) Position Description: Apply Position Description Mission The Chair of Representation Theory (headed by Prof. Andrei Negut) at EPFL (Lausanne
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University of Basel, Condensed Matter Theory and Quantum Computing Position ID: University of Basel -Condensed Matter Theory and Quantum Computing -POSTDOC [#21540] Position Title: Position Type
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also a central institution in Switzerland for the theory and practice of digital humanities and offers a master's degree and a doctorate in digital humanities. It has collaborative ties with several
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economy, game studies, and/or code studies – would be welcomed. Also desirable would be experience (or interest) in local and global community building, academic and para-academic event organization, and/or
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Your profile Candidates should have an exceptional academic record and a robust mathematical foundation. They should have published works at the main conferences in the field of machine learning, such as ICML, NeurIPS, ICLR, etc. Excellent communication skills and fluency in English (spoken and...
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conduct cutting-edge research smart contract development, and implementation for innovative applications and code analysis with the scope of on-chain data collection. The candidate would also be involved in
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, invites applications for a Postdoc position. This position offers a unique opportunity to engage in research aimed at addressing contemporary challenges in management and organizational theory. Job
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phenomenological and quantitative first-principles theory of static and dynamic chirality in crystalline materials. The specific sub-project advertised here seeks to understand the unexpectedly large measured
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areas like programming languages, algorithms, and theory. INSAIT is structured similarly to top U.S. and European research institutions and provides exceptional working conditions, in terms of facilities
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learning (RL), such as (but not limited to) Theory of online learning, reinforcement learning, and data-driven control Learning in games, and multi-agent RL RLHF and alignment in LLMs Representation learning