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areas: Computer Vision and Image Processing Machine Learning, Deep Learning, and Reinforcement Learning Large Language Models (LLMs) and Multimodal Models Generative AI, Agentic AI, Physical AI, and
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for developing and using systems to phenotype root systems in greenhouse and field settings under the guidance of research mentors. Build skills in root image processing and other data analysis activities using
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interdisciplinary team of molecular biologists, bioinformaticians, physicists, and pathologists to develop a biophysically interpretable machine learning model that integrates diverse biological data—including
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Research Center for Molecular Medicine (CeMM), ÖAW | Graz 12 Bez Andritz, Steiermark | Austria | 3 months ago
transport, metabolism, chemical biology, functional genomics, proteomics, structural analysis, bioinformatics, imaging and physiology, machine learning, drug discovery. The work for Track1 will initially be
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with programming in Python is a requirement Experience with telecentric particle imagers, image analysis, and machine learning for particle recognition is an advantage Experience of working with wave
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of molecular data. While machine learning has transformed fields such as computer vision and natural language processing, the molecular life sciences remain a rich frontier for methodological innovation
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requirement Experience with telecentric particle imagers, image analysis, and machine learning for particle recognition is an advantage Experience of working with wave flumes to study entrainment is an
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to evolutionary time scales. One of the goals of the SCINet Initiative is to develop and apply new technologies, including AI and machine learning (ML), to help solve complex agricultural problems that also depend
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design, alignment, operation, and advanced characterization techniques, including laser-based and imaging setups. Competent in scientific programming and data analysis (e.g., Python/Matlab), with strong
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, computer graphics, and embodied AI, including physics-based simulation, character animation, motion synthesis, and learning-based control for humanoid systems. Key Responsibilities: Conduct independent and