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Field
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experts in making better-informed decisions. You’ll gain expertise in 3D deep-learning, semantic technologies, explainable and uncertainty-aware AI, and human-centred evaluation, and test your work in
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have learned during the day. Using a gentle sound played during deep sleep, linked to a therapy session, we aim to help the brain hold on to the progress made in therapy. The student will use wearable
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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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” modeling solutions; we are open to and excited about applying all different types of statistical and ML techniques, from linear models to deep learning, depending on what best fits a given problem. The most
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deep generative models (VAEs, GANs, diffusion models) or probabilistic modelling is a strong plus. You have good programming skills in Python and experience with a deep learning framework such as PyTorch
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opportunities for innovation and support well-founded decision-making within the programme. You design and organise engaging workshops and learning experiences for students, connecting digital and technological
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at least one deep-learning framework (PyTorch preferred).•A solid grounding in machine learning. Experience with representation learning, generative models, foundation models or multimodal integration is a
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AITHYRA GmbH - Research Institute for Biomedical Artificial Intelligence of the Austrian Academy of Sciences | Vienna, Virginia | United States | about 3 hours 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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and support well-founded decision-making within the programme. You design and organise engaging workshops and learning experiences for students, connecting digital and technological developments to real
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foundations of medical deep learning. The project focuses on novel self-supervised objectives, information geometry, mitigating representation bias for rare pathological findings, and building next-generation