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expertise and achieve optimal results. Your Profile A PhD in Bioinformatics, Computational Biology, or a related field. Proven experience in large-scale omics data analysis, preferably MS-based proteomics
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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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-sampling data. Furthermore, the position holder will play a central role in creating high-quality training datasets (seagrass maps) to support artificial intelligence (AI) algorithms used in related projects
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at both large and small scales. The scientific evidence-based knowledge developed in ISOLUME will be used to develop a roadmap for implementing changing marine lightscapes as an indicator in management
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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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, survey) or applied microeconometrics, and applied economics. You have experience with big data and machine learning methods? This would be a particular asset! With excellent English language skills, both
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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
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(e.g. Python, R, …). Familiarity to work on a Linux computing cluster (HPC). Preferably experience in working with large medical image data. Vivid interest in the analysis of microscopy images or similar