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learning models Implementation of deep learning Improvement of models, e.g. in terms of efficiency, training performance or inference behavior We promise you excellent contacts in the relevant industrial and
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are using ferroelectric memories, which can calculate AI algorithms from the field of deep learning in resistive crossbar structures with extremely low power consumption and high speed. Furthermore, we
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PhD position in interpretable machine learning for dementia prediction. The project focuses on developing interpretable deep learning models for dementia prediction using multi-modal data, including MRI
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in machine learning Extensive experience with either computer vision or image analysis Good knowledge of deep learning packages, PyTorch Familiar with foundation models (vision large models or multi
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(preferably in Python) Very good knowledge in German or English Please feel free to apply for the position even if you do not have all the required skills and knowledge. We may be able to teach you missing
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. Focus on building deep, strategic, long-term relationships with internal & external stakeholder to be viewed as a partner rather than transactional. Be a “partner in the trenches”—be responsive, engage
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proliferation. The successful candidate should have prior experience in handling genomics, transcriptomics, and single-cell omics datasets. Candidates with sufficient experience in machine learning and deep
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holistic view of interconnected biological systems in health and disease. We develop clearing technologies for cellular-level imaging and deep learning algorithms (AI) to analyze large imaging and molecular
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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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science, and applied plant research Example reading: Peleke, F. F., Zumkeller, S. M., Gültas, M., Schmitt, A., & Szymański, J. (2024). Deep learning the cis-regulatory code for gene expression in selected