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. This project will develop and apply new computational/analytical tools to guide XFEL experiments for specifically tracking lattice fluctuations and ion dynamics in energy materials (batteries). The project will
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The doctoral student project and the duties of the doctoral student This Data Driven Life Sciences (DDLS) PhD project focuses on probabilistic models of protein structure, which can be used primarily
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for global industries, all while collaborating with experts in the prestigious International Systems Realisation Partnership. Furthermore, the student will gain invaluable skills in data analysis, problem
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relevant discipline/subject area. A minimum of English language proficiency (IELTS overall minimum score of 6.5). Also, the candidate is expected to: Have excellent analytical, reporting and communication
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technology and economics Experience in energy system modeling is beneficial First programming skills, ideally in Python Independent and analytical way of working Reliable and conscientious working style Fluent
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check our website for more information: https://www.uni-goettingen.de/de/635183.html PhD students with their own funding (e.g. DAAD) can join at any time. Tuition fees per semester in EUR None Combined
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analytical models, then informing a refined acoustic model taking both into account with data-driven approaches. This research is inherently multidisciplinary, lying at the interface of fluid and solid
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completed scientific university education (Master's/Diploma) in food chemistry, chemistry or a related field sound knowledge and experience in the use of instrumental analytical methods such as HPLC-MS, GC-MS
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research department Virology is your home base for the execution of preclinical and clinical studies in viral infection models. Your qualities You are a strategic thinker with strong planning and analytical
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conducting experiments and performing analytical sample processing (lab work); a theoretical background in marine environmental physiology and biogeochemistry; willingness to experiment with new data