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Your Job: The conventional, manual co-design of algorithms and hardware is slow and inefficient. Our group develops methods and tools to automate the co-design process. The core of this project is
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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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Your Job: Explore bio-inspired algorithms through simulation—both numerical and circuit-based—and experiment with existing hardware, including CMOS and memristor circuits. Additionally, will need
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inviting applications for a PhD Student (f/m/x) for the project Theory and Algorithms for Structure Determination from Single Molecule X-Ray Scattering Images Project description Single molecule X-ray
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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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of algorithms and digital neuromorphic hardware is an additional avenue for enhancing the efficiency of the methods. In this context the research will explore digital, event-based implementations
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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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algorithms for application parallelization, simulators and virtual platforms for application- and architecture exploration, hardware/software co-design and operating/runtime systems. Typical application
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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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computing to develop a continuous and local alternative to existing gradient-based learning rules, bridging theories of predictive coding with event-based control/ Simulate models of the learning algorithm