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spoken English skills Following competences are desirable: Solid knowledge of solid-state (semiconductor) physics Good knowledge in surface science Experience in epitaxy and surface science methods Basic
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: Master’s degree in a scientific or engineering subject with excellent grades; enthusiasm for neuroscience and brain mapping; openness to new methods such as deep learning. Offer: Funded PhD position (TV-L 13
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challenges in energy, mobility, and sustainability. Traditional trial-and-error methods in materials design are often too slow, costly, and inefficient to cope with the increasing complexity of performance and
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techniques is highly desirable and knowledge of basic microscopy methods is an advantage. Interest and/or experience in working at the interface between wet and dry lab is a plus. Interest in working
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cytometry methods Experience with cell culture techniques is highly desired Previous experience with genomics approaches and data analysis is a plus. Interest in working in a project with wet and dry lab
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the Leibniz Junior Research Group “ Probabilistic Methods for Dynamic Communication Networks“ (Head: Prof. Dr. B. Jahnel) starting as soon as possible. The position is within the Math+ project "Information Flow
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reflects the organic pollution load in water. Current COD determination methods are often discontinuous or spectroscopic and impractical for real-time application, limiting their utility in dynamic, data
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, offering targeted training in research methods, project management, and leadership skills. This ensures you graduate not only as a specialist in your field but also as a well-rounded professional. Global
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entities come together to create living cells? Combining traditional methods, from structural to synthetic biology, with novel computational approaches, chemists, physicists, and molecular biologists
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hydrological modelling methods that explicitly use interpolation to pre-process precipitation data. In addition, the research should explore the integration of opportunistic sensor data to improve forecast