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. Central to its objectives is the development of methods for culturing tailor-made organoids, assembloids and co-organoids for inter-organ communication towards AI-supported large-scale / high-throughput
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at large scale facilities Establishment of cooperation projects with energy-related institutes at Forschungszentrum Jülich Initiating grant applications Supervision of MSc and BSc students Presentation
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models using experimental data for precise mapping of real processes Conducting detailed analyses of thermomechanical stresses in electrochemical converters using the finite element method (FEM
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of a project with limited experimental data points? In addition, how would you combine various computational chemistry methods that can leverage data to enhance potency predictions? With your solution
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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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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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for data-efficient exploration and optimization within the process parameter space as well as for adaptive, data-driven machine learning to map the electrolysis process to a digital twin. Data workflows and
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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
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physics or related with a background in the field of experimental quantum information Willingness to work in laboratory and cleanroom environments Ideally, initial experience in a technical or scientific
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on the design and evaluation of innovative data- and machine learning-based systems to integrate more renewable energy into our energy systems and make energy use more efficient. We develop new optimization