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to the development and implementation of approaches that interface with living systems through novel materials and algorithms, electric and magnetic fields, ultrasound, optics and targeted radiation, microfluidics
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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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methods/algorithms such as evolutionary and quality-diversity search, generative ML models, hybrid models, bio-inspired approaches, multi-agent systems Experience with real-time or embedded systems
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: Develop and apply evolutionary algorithms to jointly optimize both the robot’s morphology and autonomy, and apply quality-diversity methods to discover a wide range of high-performing designs. Work
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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 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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of the candidate and the 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
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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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algorithms, electric and magnetic fields, ultrasound, optics and targeted radiation, microfluidics, controlled force sensing and actuation and related tools for probing and controlling biomolecular systems