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from backgrounds, including computational chemistry, bioinformatics, systems biology, physics and machine learning. The project offers a unique opportunity to collaborate closely with experimental
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from backgrounds, including computational chemistry, bioinformatics, systems biology, physics and machine learning. The project offers a unique opportunity to collaborate closely with experimental
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that combine deep learning, computer vision, and bioinformatics to extract actionable insights from complex, multi-modal data, including medical imaging, genomics, and clinical records. A central theme of our
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to the project’s scope, such as mechanistic interpretability of LLMs, robustness verification of machine learning models, and conformal inference. Applicants should demonstrate scientific creativity, research
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AI technologies. We develop innovative approaches that combine deep learning, computer vision, and bioinformatics to extract actionable insights from complex, multi-modal data, including medical
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more effective screening and therapy. The postholder will focus on developing and applying advanced computer vision and machine learning methods for multimodal imaging and real-time analysis in
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intelligence experts to generate new projections of the land ice contribution to sea level rise until 2300 with machine learning. You will develop probabilistic machine learning “emulators” of multiple ice sheet
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. DVXplorer), and tactile/force sensors. Strong background in computer vision and deep learning, with practical implementation experience. Proficiency in programming with C++ and Python, including use of ROS
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climate scientists and artificial intelligence experts to generate new projections of the land ice contribution to sea level rise until 2300 with machine learning. You will develop probabilistic machine
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adapt advanced machine learning frameworks (SPARKS and CEBRA) for supervised and unsupervised analysis of high-dimensional neural data to decode multisensory information Investigate how neural