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algorithms, preferably in the domain of QC, such as QAA and QAOA Experience in cloud computing and building cloud computing infrastructure Interest and/or experience in developing and conducting lectures and
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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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for the physical sciences. We have a strong profile in computational statistics, simulation and learning algorithms, and scientific software development. As a closely collaborating, international team
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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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on multi-view sensor perspectives of the “First Mile” test field validation of the developed algorithms and execution of tests on the test bed “Erste Meile” What you bring to the table student in Computer or
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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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21 Aug 2025 Job Information Organisation/Company TU Dresden Research Field Computer science » Other Researcher Profile Recognised Researcher (R2) First Stage Researcher (R1) Established Researcher
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) Country Germany Application Deadline 6 Sep 2025 - 21:00 (UTC) Type of Contract To be defined Job Status Other Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is
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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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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