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hearing loss. However, current neural devices are large, complex, and invasive, and are therefore used by only a fraction of people who could benefit from them. The goal of NANeurO is to design new
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science, automation science, or a related field, and convincing expertise in robotic hardware. Experience with machine learning and large language models is highly desirable. Prior experience in a biological setting
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, probabilistic models Representation learning, self-supervised learning, foundation models Data analysis, non-linear statistics, knowledge management Your profile PhD in Computer Science, Bioinformatics
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integrate large-scale sequence and RNA-seq data from internal and public resources. You build a reference library of predictive regulatory motifs. You use network analysis and random-forest approaches
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European sea basins over decadal timescales, due to coastal darkening (COD) and artificial light at night (ALAN), and will determine drivers, sources and impacts of these changes at both large and small
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us We are TUM’s unique Pathology AI lab developing new machine learning (ML) methods for automatically analyzing digital pathology data and related medical data. Such methods include the automatic
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-reconstructions and observations, low-order data assimilation, or deep neural networks. A quantification of the impact of mesoscale and submesocale features is also expected. At a later stage, the successful
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tools Supervising and guiding Master and PhD students Active participation in project meetings and events Presenting and publishing the research on an international stage Your Profile: As part of our
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Heidelberg University and Stanford University, including population health researchers, clinicians, and methodologists. The researcher will lead analyses in large-scale electronic health record data
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focus on neutron spectroscopy as main analysis technique, supported by complementary experimental techniques or theoretical simulations Hands-on participation in experiments at large scale facilities as