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Your Job: In this position, you will be an active part of our AI Consulting Team. Together with our partners, we develop new and innovative applications of Machine Learning. You will connect
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description: Join the new and highly motivated, international research group on Machine Learning and Robotics at the Faculty of Mechanical Engineering at KIT. The mission of the Machine Intelligence and
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to develop a 3D-generative algorithm for pharmaceutical drug design by using or combining novel machine learning approaches? How would you integrate machine learning, physics-based methods in an early-stage
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-processing, and machine learning textual analysis of the full text of policy documents. Qualitative content thematic analysis is envisioned to compliment structural topic modelling to identify strategies and
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the risks and success rates of real, patient-specific aneurysms, their treatment options, and long-term prognosis. The project is complemented by contributions in machine learning, such as the rapid
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collaborative project between the Ralser lab and the Vingron Lab. This joint endeavor aims to explore phenotype predictions based on large proteomic datasets and machine learning approaches. We are seeking a
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. The position is part of the project “Understanding of, and Explanations with, Large Language Models”, which is funded by the Volkswagen Stiftung and associated with the Cluster of Excellence “Machine Learning
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theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine learning. It also offers the opportunity to work with
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your technical goals. Collaboration opportunities with the Nanoscale Science Department and the European industry leaders in electron microscopy and machine learning, as well as financial support to
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Max Planck Institute for Multidisciplinary Sciences, Göttingen | Gottingen, Niedersachsen | Germany | 19 days ago
the structure from such data is challenging, and new theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine