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analyzing developmental-oncogenic signaling pathways using a combination of experimental and computational approaches. Within this project, we are creating perturbation-based genetic maps at the level of
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-based tiles can be arranged and actuated to form tunable metapixels, enabling dynamic control of light at the nanoscale. This project will integrate algorithmic self-assembly and nanomechanical switching
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decision-making algorithms on real robotic systems operating in unstructured and dynamic environments. This work is connected to the Robotics Institute Germany (RIG) and relates to the thematic cluster
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candidate will join an exciting research program exploring a newly emerging paradigm in immunology: mitochondrial transfer and synthetic organelle therapy. Using newly developed genetically engineered mouse
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spanning multiple locations and entities, where complex constraints and resource interdependencies – among people, machines, and robots – demand the deployment of intelligent algorithms for orchestration
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-1,3,3,3-tetrafluoropropene (R1234ze(E)). The position combines mechanism building and validation with algorithm and database contributions to RMG, supported by electronic-structure data from literature, and
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underlying neurovascular disease and secondary neurodegeneration. Our goal is to uncover how genetic defects of brain endothelial cells and mural cells contribute to vascular dysfunction. We further wish
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machine learning-based systems to integrate more renewable energy into our energy systems and make energy use more efficient. We develop new optimization methods, machine learning algorithms, and
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as well as of the RWTH Aachen. Our central objective is to elucidate the genetic, molecular, and cellular mechanisms governing the formation and function of the testis, production and function of sperm
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polarization of mutations in cancer genomes (Aitken 2020, Anderson 2024), as well as how genetic background shapes the trajectory of cancer evolution (Aitken 2025). We also repurpose technologies like