14 evolutionary-algorithms-"RMIT-UNIVERSITY" PhD positions at Technical University of Munich
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PhD Position in Theoretical Algorithms or Graph and Network Visualization - Promotionsstelle (m/w/d)
31.07.2025, Wissenschaftliches Personal The Chair for Efficient Algorithms, led by Prof. Stephen Kobourov, is inviting applications for a fully funded PhD position at the Technical University
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21.12.2021, Wissenschaftliches Personal The Department of Computer Science, Technical University of Munich, has a vacancy for a PhD candidate/researcher position in the area of efficient algorithms
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starting date is November 2025. The topic of the PhD project will be theoretical research in discrete optimization, with a particular focus on either graph algorithms or multiobjective optimization
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13.01.2020, Wissenschaftliches Personal PhD position at the Chair of Algorithms and Complexity. Candidate shall work on approximation algorithms for scheduling problems in parallel and distributed
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motivation in conducting evolutionary and chemical ecological research at different levels. •MSc/diploma in a relevant field (e.g., evolutionary biology, chemical ecology, sensory biology). •Strong experience
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programming and know how to use version control. ▪ You are experienced in the usage of machine learning (e.g., Actor-critic algorithms, deep neural networks, support vector machines, unsupervised learning
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application from qualified women. About the position The position involves both teaching and engaging in innovative research projects on tractor autonomy, path-planning algorithms, soil compaction modeling, and
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05.04.2023, Wissenschaftliches Personal We are the Autonomous Vehicles Systems (AVS) Lab and are interested in the algorithmic foundations of path and behaviour planning, control and automated
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05.04.2023, Wissenschaftliches Personal We are the Autonomous Vehicles Systems (AVS) Lab and are interested in the algorithmic foundations of path and behaviour planning, control and automated
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03.06.2021, Wissenschaftliches Personal The Albarqouni lab develops innovative deep Federated Learning (FL) algorithms that can distill and share the knowledge among AI agents in a robust and