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AITHYRA GmbH - Research Institute for Biomedical Artificial Intelligence of the Austrian Academy of Sciences | Vienna, Virginia | United States | 3 days ago
(e.g. geometric deep learning, generative models, representation learning) to decode molecular physiology, pathology, and therapeutic design? Are you passionate about high-throughput biology and scalable
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Are you passionate about combining the directed evolution of diverse biomolecules with deep learning approaches and contributing to the development of better (bio)catalysts and drugs? We
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intelligence, data science, medical physics, neuroimaging, bioengineering, or related disciplines, accompanied by accredited training in machine learning, deep learning, or medical image analysis. Experience: A
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the traditional custodians of the land, sea and waters of the areas upon which we live and work. We recognise their valuable contributions and deep connection to country and pay respect to Elders past
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archaeological signatures (e.g., micro-relief, edge structures, etc.) – Design and implementation of new deep learning architectures (both supervised and unsupervised/few-shot, 2D and 3D) for an efficient and
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coworkers in the methodologies. Required Qualifications: • Master’s Degree in Biomedical Engineering/Computer Science/Neuroimaging • Strong understanding of machine learning, deep learning for biomedical
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custodians of the land, sea and waters of the areas upon which we live and work. We recognise their valuable contributions and deep connection to country and pay respect to Elders past and present. Use your
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-of-the-art bioinformatics approaches, increasingly incorporating AI and deep learning (see, e.g., Sarropoulos et al., Science 2026). This work has provided insights into the origins and functional evolution
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., long-read spatial transcriptomics) across diverse organs, from the brain to the gonads, with state-of-the-art computational and deep-learning approaches. Projects may also include detailed molecular and
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them using state-of-the-art bioinformatics approaches, increasingly incorporating AI and deep learning (see, e.g., Sarropoulos et al., Science 2026). This work has provided insights into the origins and