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) information-theoretic active learning, and c) capturing uncertainty in deep learning models (including large language models). The successful postholder will hold or be close to the completion of a PhD/DPhil in
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analysis of large data sets Prior experience in signal transduction research Knowledge, Skills and Abilities: PhD level knowledge and understanding of basic cellular and molecular biology Expertise in cell
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environment of the institute. Opportunities Access to a unique and comprehensive dataset combining genomic and clinical information from a large cohort of cancer patients. Potential to establish an independent
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) through advanced computer vision and LVLM integration; (b) overcome technical challenges: innovate solutions for large-scale small-object detection, dynamic lighting adaptation, and edge-computing
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construction activities that are associated with the disciplined allocation of capital to large-scale infrastructure projects by corporations. The analysis is expected to identify key development
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Max Planck Institute for Brain Research, Frankfurt am Main | Frankfurt am Main, Hessen | Germany | about 2 hours ago
candidate has excellent quantitative and data analysis skills, a proven ability to work independently, and a collaborative mindset. They will be expected to lead their own research projects, contribute
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large multi-dimensional datasets using statistical tools such as positive matrix factorization (PMF) and cluster analysis Investigate the influence of different urban emission sectors on atmospheric
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Cutting Theme 2 (Satellite Remote Sensing). The EOS-RS teams’ research covers SAR, multi/hyper-spectral remote sensing, LiDAR, and GNSS data for disaster response and hazard monitoring for earthquakes
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invasive sensing tools to monitor metabolites, oxygen, carbon dioxide, pH, and other parameters. Ideally, the methods can function in parallel and on a large scale. The research is vital to understand key
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. The Research Assistant/Research Associate will play a key role in technical and scientific work of unifying large scale genomic (WGS, GWAS, long read) and non genomic (EHR, lifestyle, clinical) data from