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Field
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-edge Machine Learning applications on the Exascale computer JUPITER. Your work will include: Developing, implementing, and refining ML techniques suited for the largest scale Parallelizing model training
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Computer-adaptive methods and multi-stage testing Application of machine learning in psychometrics Predictive modeling of educational data Methodological challenges in cohort comparisons Advanced meta
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profile: Successful degree (Master's, Diploma) in the field of computer science, software systems engineering, digital technologies or comparable qualification Openness to varied and exciting topics and
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that are technically well-grounded and at the same time represent stakeholder preferences. The integrated Research Training Group (RTG) will provide doctoral researchers with an attractive qualification program, foster
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genetics, genomics, imaging processes, computational biology and biochemistry. Our goal is the deep and detailed understanding of fundamental mechanisms in plant biology, which may also then be used
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the use of machine learning methods to process complex data sets. The focus is on techniques such as ultrasound, radar, computed tomography, acoustic emission analysis, and infrared thermography
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varieties arising from zero-mutable Laurent polynomials. Prior experience in any of these areas as well as an interest in combinatorial and computational methods is welcome, but not required. What we offer
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to the table You have a master's degree (technical college, technical university, university) in mechanical engineering, materials engineering, materials science, physics, electrical engineering, computer
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to the table You have a master's degree (technical college, technical university, university) in mechanical engineering, physics, computer science, electrical engineering, materials engineering, materials
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interested in working at the boundaries of several research domains Master's degree in computational biology, bioinformatics, systems biology, bioengineering, chemical engineering, or a related discipline