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28.05.2025, Wissenschaftliches Personal We are looking for talented and ambitious scientists interested in unique interdisciplinary research, integrating machine learning, molecular simulations
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of research focus include machine learning/learning analytics, multimodal assessment, adaptive learning in online settings, and the role of self-regulation in learning with AI. Close networking with
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the research area of adversarial robustness in LLMs as a Doctoral candidate / PhD Student (f/m/d) At the chair of Data Analytics and Machine Learning at the Technical University of Munich (TUM), a full position
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macromolecular dynamics with statistical mechanics, molecular simulation at different resolutions, machine learning, and experimental data. Our group works on the definition and implementation of strategies
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statistics, bioinformatics, machine learning and AI applications. Experience in a number of these technologies is expected. Collaborations within the Cluster of Excellence ImmunoSensation and with other intra
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such as ecology, economy and social sciences. ZMT aims to use data science tools, including computer vision and deep learning, for the study of rapid changes in tropical coastal socioecological systems
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maintain pipelines for the analysis of high-throughput sequencing data, including RNA-seq, ChIP-seq, ATAC-seq, and single-cell and spatial omics. Integrate machine learning and large language models (LLMs
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Your Job: Scientific and technical lead of a team focusing on machine learning and big data analytics in X-ray science Development and application of machine learning tools for X-ray data analysis
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. Bringing together internationally leading expertise in Climate Modelling, Earth Observation, and Machine Learning, research at the center will advance our modelling capacity of the Earth’s climate and
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Max Planck Institute of Immunobiology and Epigenetics, Freiburg | Freiburg im Breisgau, Baden W rttemberg | Germany | about 1 month ago
of high-throughput sequencing data, including RNA-seq, ChIP-seq, ATAC-seq, and single-cell and spatial omics. Integrate machine learning and large language models (LLMs) into bioinformatics workflows