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disciplines (typically mathematics, physics). For Postdocapplicants: Excellent track recordin computer science or engineering. Fluency in spoken and written English is required. Proficient in at least one
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Researcher / Postdoc for molecular investigations on microbial ecology in deep-sea polymetallic n...
information collected from molecular data and explore in combination with existing knowledge on environmental conditions using ecological statistics Characterize environmental consequences of nodule mining and
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scaling and generalization behavior Roll out the model to the global user community Requirements PhD or MSc in computer science, physics, mathematics or a related discipline Experience with large-scale HPC
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performance and degradation of electrolysis in dependence on different operating modes Your Profile: Completed Master’s degree in chemical engineering, computational engineering, computational mathematics, data
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Overview Eine Druckversion dieser Ausschreibung (PDF) steht Ihnen in den weiteren Registerkarten (z.B. „Bewerbungsvoraussetzungen“) zur Verfügung. A print version of this announcement (PDF) is available in the further tabs (e.g. “Application requirements”). Programme Description The Pre-Doc...
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of targeted therapies. Analyzing high-dimensional single-cell data has its own statistical and computational challenges, and standard tools often cannot be applied. The purpose of the position and goal
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good publication track record Above-average master’s degree in computer science, electrical/ mechanical engineering, applied mathematics, or a similar engineering-oriented quantitative discipline
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requirements: • PhD diploma in related field • Excellent skills in statistical analysis • Very good knowledge of English; spoken German is a benefit • Excellent scientific and writing skills Who we are: You will
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experience (possibly as part of the PhD) in a field related to mathematical analysis of partial differential equations (PDEs), calculus of variations, optimal control, who is willing to engage in innovative
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required to create a holistic picture. Such additional information can improve the performance, help to reveal biases, or may enable to perform causal inference. We are interested in developing statistical