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architectures, implement co-evolutionary algorithms, and develop rigorous evaluation frameworks measuring adversarial robustness. Outputs include an agent-based simulation toolkit, stress-testing methods, and
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: The design and analysis of computational methods that accelerate AI/ML when applied to large scientific data sets; Energy efficient physics-aware algorithms, capable of distributed learning on high performance
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topics, in collaboration with the rest of the team: Develop and apply evolutionary algorithms to jointly optimize both the robot’s morphology and autonomy, and apply quality-diversity methods to discover a
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will work on the following topics, in collaboration with the rest of the team: Develop and apply evolutionary algorithms to jointly optimize both the robot’s morphology and autonomy, and apply quality
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of areas, including AI and machine learning, cloud and mobile computing, computer system and information security, evolutionary computation, computer vision and graphics, and bioinformatics
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to the research in NCEI, as described in the Project Description above. Experience with AI methods/algorithms such as evolutionary algorithms, quality-diversity methods, and evolutionary robotics. Experience with
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needs of the research center. More specifically, the postdoc will work on the following topics, in collaboration with the rest of the team: Develop and apply evolutionary algorithms to jointly optimize
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6 Feb 2026 Job Information Organisation/Company Delft University of Technology (TU Delft) Research Field Computer science » Programming Mathematics » Algorithms Mathematics » Discrete mathematics
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for creative or artistic purposes PhD thesis relevant to the research described in MishMash WP1 Experience with AI methods/algorithms such as evolutionary and quality-diversity search, generative ML models
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of algorithms and models to realistically simulate forest ecosystem dynamics under varying conditions of land use change, forest and land management, climate variability, and other environmental stressors