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to make viable trading decisions under high price volatility. This PhD position focuses on designing, developing, and evaluating self-learning energy trading algorithms that are able to cope with
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. Daniel Merkle. The overall research project is based on the novel application of formalisms, algorithms, and computational methods from computer science to the design of microbial communities, ultimately
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. Daniel Merkle. The overall research project is based on the novel application of formalisms, algorithms, and computational methods from computer science to the design of microbial communities, ultimately
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, which has multiple test machines with GPUs and AI accelerators. The algorithms used can be bound by the available compute power or memory bandwidth in different parts of the program. This information will
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algorithms. The targeted starting date is 1 September 2025,or as soon as possible thereafter. Project description This project will explore the algorithms, advantages, and applications of quantum computing
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Approximation. Parameterized Complexity is a vastly growing area within theoretical computer science that allows for the development of exact and approximation algorithms for computationally hard problems by
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PhD Position in Theoretical Algorithms or Graph and Network Visualization - Promotionsstelle (m/w/d)
interfaces. Topics of interest include: Planar and geometric graph algorithms Approximation and parameterized algorithms Clustering, embeddings, and structural graph theory Computational complexity and
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Apply now The Faculty of Science, Leiden Institute of Advanced Computer Science, is looking for a: PhD Candidate, Efficient LLM Algorithm, Hardware and System Design (1.0 FTE) Project description We
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Starting Date 1 Jan 2026 Is the job funded through the EU Research Framework Programme? Horizon Europe - MSCA Marie Curie Grant Agreement Number 101227453 Is the Job related to staff position within a
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integrates machine learning and statistics to improve the efficiency and scalability of statistical algorithms. The project will develop innovative techniques to accelerate computational methods in uncertainty