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, evolutionary histories, or domain architectures as potential drivers of altered bacterial virulence. You will apply supervised and unsupervised machine learning to correlate adhesin variation with virulence and
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Your Job: In this position, you will be an active member of our "Simulation and Data Lab for AI and Machine Learning in Remote Sensing", which aims to strengthen interdisciplinary research by
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of distributed computing, machine learning, image and text analysis, randomized data structures, high-performance computing, and quantum algorithms. Beyond this research, we aim to support computational thinking
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, storage, accessibility/sharing, archiving, publication, and preparing data for machine learning applications. The Research Training Group RTG 3120 offers, subject to the availability of resources, a
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the organizational activities and implementation of the Cluster’s goals. Further information about the Cluster can be found at https://uni-tuebingen.de/en/research/core-research/cluster-of-excellence-machine-learning
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, computational biology, computer science, or related fields. Proven expertise in machine learning, LLMs, or deep learning architectures and their application to biological or biomedical data. Experience in
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development, proliferation, and evolution. To strengthen our expertise in bioinformatics and machine learning, we are offering a position for a highly motivated scientist with significant experience in
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research experience, preferably in programming languages, compilers, applied mathematics, and optimization techniques a strong background in compiler, code generation, and machine learning would be
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the application and development of modern methods in data science and machine learning, especially with unstructured data. A good command of the German language is essential due to the focus of the academy project
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systems. We intend to develop ground motion models that integrate large-scale databases of observed ground motions, physics-based simulations of seismic waveforms and cutting-edge machine learning methods