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therapeutics by protein design. This project will apply cutting-edge generative AI methods—including protein design, structure–function prediction, and multimodal learning—to develop and optimize a new
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++, Python, and JavaScript languages, multi- and many-core SoC, RISC-V, hardware synthesis, hardware-software co-design, (meta-heuristic) optimization algorithms, machine learning frameworks, (bonus topics
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are being developed that provide AI-supported tools to identify suitable sources and optimize utilization decisions throughout the product life cycle. Various machine learning approaches are to be used
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These are positions for Doctoral Students, based in Tübingen in an interdisciplinary research group working at the interface of Machine Learning, Medicine, and Biology. Doctoral Students will engage
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and methodological skills with a focus on quantitative data analysis (e.g., econometrics, statistics, machine learning) a high motivation and the ability to work independently with a strong team
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approaches Encouragement to pursue own research ideas Opportunity to develop an independent scientific profile Possibility to acquire funding to support future projects REQUIREMENTS: Highly motivated and
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analysis (e.g., econometrics, statistics, machine learning) A high motivation and the ability to work independently with a strong team orientation Excellent spoken and written English and the will
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, agricultural sciences with a focus in economics, or related disciplines strong analytical and methodological skills with a focus on quantitative data analysis (e.g., econometrics, statistics, machine learning) a
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Your Job: In this position, you will be an active part of our "Simulation and Data Lab Applied Machine Learning". Within national and European projects, you will drive the development of cutting
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analytical and methodological skills with a focus on quantitative data analysis (e.g., econometrics, statistics, machine learning) a high motivation and the ability to work independently with a strong team