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Field
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Performing atom probe tomography Application of deep learning algorithms for the correlation of the obtained experimental data and their analysis The majority of the work will be undertaken in Hamburg Your
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requirements are Experience in working with large-scale spatial-temporal traffic and/or travel behavior data, e.g., loop detector, floating car data, GPS data, cellphone data. Experience with transport
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Dynamics (W-0042 | all genders) to head the Weather-Climate Interaction Group, which aims to advance the fundamental understanding of how small-scale processes influence large-scale climate patterns
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enable the model to infer health-related information directly from NMR spectra of human blood. To this end, the model will be pre-trained using self-supervised learning on large-scale, partly synthetic
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. Furthermore, we develop advanced scattering methods and machine learning tools for data analysis. For more information, see www.soft-matter.uni-tuebingen.de Qualification and skills Candidates should have good
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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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degree in Physics, Materials Science, Computer Science, Data Science, or related fields Proven experience with large language models (LLMs), natural language processing (NLP), and fine-tuning techniques
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from infancy to adulthood - Deep neural network-based encoding models of the human visual system using large neuroimaging datasets (fMRI/ EEG, precision neuroscience) - Advanced analyses of the structure
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geothermal energy systems. We develop and use AI-based applications with a focus on the development of analytical methods for the analysis of large data sets, feature extraction, automatic pattern recognition
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Your Job: Fine root structures are inexpensive for plants in terms of metabolism and biomass (allocated carbon and nutrients), but at the same time they contribute a large proportion of nutrient