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data at an internationally competitive level. Experience of biostatistics or machine learning approaches Proficiency in a scripting language like R or Python, as well as ability to work efficiently in a
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candidate will collaborate with various research groups, expand professional network and acquire advanced biochemical and computational skills. This project builds upon our team’s established expertise
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: developing and applying image analysis and sensor technologies (e.g. RGB/NIR) for textile production, as well as using machine learning for process optimisation and performance prediction from fibre
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researchers from literacy, STEM, special didactics, and the science of learning to create an interdisciplinary environment applying evidence-based practice. By systematically developing and implementing
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of complex brain processes. The prospective PhD candidate collects brain MSI data and develops novel machine learning methods in connection to generative models such as flow matching. Therefore, the doctoral
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experience in the design of therapeutic antibody modalities Documented research experience in AI/machine learning/deep learning Documented experience in the development of therapies for oncological
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will also use focussed ion beam milling scanning electron microscopy (FIB-SEM) to prepare infected cells for in situ cryo-ET. The resulting tomographic data will be analysed by machine-learning assisted
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, the following are required: – Documented several years of experience in training, evaluating, and deploying machine learning models, including deep neural networks and relevant frameworks – Documented several
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at the intersection of numerical analysis, uncertainty quantification, and scientific machine learning. The research will primarily focus on probabilistic methods for data-driven model reduction, with
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focus on innovative development and application of novel data-driven methods relying on machine learning, artificial intelligence, or other computational techniques. The applicant is expected to develop