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Max Planck Institute of Molecular Cell Biology and Genetics, Dresden | Dresden, Sachsen | Germany | 3 months ago
. Experience in machine learning algorithms and tools. Experience in microscopy and image analysis. Effective collaboration in interdisciplinary teams. Strong analytical reasoning and persistence in experimental
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to effectively learn this technique will be needed. Effective oral and written communication skills. Must be computer literate with proficiency and working knowledge of database and reporting tools such as
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(e.g. Python, Julia), You have an interest in, or experience with, machine learning approaches for scientific data analysis. You have a strong interest in interdisciplinary research and enjoy
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Qualifications The following qualifications and experience will be considered an advantage: Experience with crop modeling. Experience with plant breeding. Background in data science, machine learning, and
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research studies, please visit the website: https://innovation.umn.edu/developmental-cognition-and-neuroimaging-lab/ The scholar will be able to draw on world-class neuroimaging facilities and training
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knowledge of developing AI-based information systems, such as AI-enabled engagement e.g., agentic AI and AI-enabled insight generation e.g., machine learning analytics in both individual and organizational
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status, sexual orientation, gender identity, or gender expression. To learn more about diversity at the U: http://diversity.umn.edu Employment Requirements Any offer of employment is contingent upon
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measurements. Have a background in computational biology, bioinformatics, or machine learning for biological problems. Have experience with modern deep learning frameworks (PyTorch, JAX, or equivalent). Have
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participant outcomes. The project will use a variety of approaches, including human perceptual experiments, machine learning, digital signal processing, and computational models of hearing. UConn has a vibrant
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practice-engaged research on how engineers learn, how engineering knowledge and identities are formed, how we assess and evaluate learning, and how educational systems can be designed and transformed