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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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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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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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Biologist (f/m/d) with a focus on proteomics, machine learning, and computational biology (up to E13 according to the TVöD Bund salary scale). The position The successful candidate will be part of a dynamic
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Max Planck Institute for Solid State Research, Stuttgart | Stuttgart, Baden W rttemberg | Germany | 2 months ago
Planck Society to support your technical goals. Collaboration opportunities with the Nanoscale Science Department and the European industry leaders in electron microscopy and machine learning, as
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-scale controllable, and cost-efficient disease models by bringing together experts in physical chemistry, physics, bioengineering, molecular systems engineering, machine learning, biomedicine, and disease
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to generate reproducible, micrometer-scale controllable, and cost-efficient disease models by bringing together experts in molecular systems engineering, machine learning, biomedicine, and disease modeling