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questions include automated modeling and model simplification/refinement supported by generative AI, system identification, and 3D reconstruction algorithms. Additionally, the research involves developing
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management solutions based on conceptual theory and empirical eco-evolutionary, molecular, and genetic data that can meet the needs of current and evolving plant production systems. Read more about our
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provides a unique opportunity to work at the intersection of AI and experimental science, combining fundamental algorithmic development with real-world applications in scientific imaging. Due to limitations
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intersection of AI and experimental science, combining fundamental algorithmic development with real-world applications in scientific imaging. Due to limitations in electron dose and scan stability, microscopy
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of Systems and Control, we develop both theory and concrete tools to design systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and algorithms
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general problem for all life that underwent endosymbiosis at some time during their evolutionary history. Depending on the applicant’s interests, the project can take several directions. One option is to
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-organelle coordination. Besides plant cells, such coordination is a general problem for all life that underwent endosymbiosis at some time during their evolutionary history. Depending on the applicant’s
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. The successful candidate holds/or are about to receive a Master of Science degree in computer science, data science, or a related area, and have strong background in algorithmic design, data mining, machine
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of Systems and Control, we develop both theory and concrete tools to design systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and algorithms
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mathematics. The applicant should be skilled at implementing new models and algorithms in a suitable software environment, with documented experience. Experience in applying or developing machine learning