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(scRNA-seq) data, and structural data from cnidarians, and we will develop new algorithms to analyze the evolutionary history of muscle components. You will study the evolution of muscle components during
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analysis pipelines. Skills and knowledge needed for this position include scripting and computer programming, bacterial genomics, and evolutionary biology and/or population genetics. Responsibilities Dissect
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‑world adoption. Role Overview We are seeking a postdoctoral researcher to define and execute an ambitious research agenda at the intersection of generative AI and algorithm-hardware co‑design for next
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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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forecasting/uncertainty. Holger Maier is internationally recognised for developing and applying AI/machine learning, optimisation (notably evolutionary algorithms), and decision-support methods for complex
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well as on their application in radar and telecommunications scenarios. The techniques will be developed within the System-by-Design paradigm and will rely on the integration of single- and multi-objective evolutionary
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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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/research/ ). About the research project This highly interdisciplinary project focuses on studying genetic variation in noncoding regions of the human genome by leveraging evolutionary information and
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IFREMER - Institut Français de Recherche pour l'Exploitation de la MER | Nantes, Pays de la Loire | France | 25 days ago
evolutionary processes in aquatic environments and their interfaces, from headwaters to the ocean, assessing ecosystem status, and developing scenarios for their future evolution. The PhD project will be
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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