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-driven approaches to health, society, and policy. BISI combines expertise in epidemiology, biostatistics, health economics, and machine learning to tackle complex societal challenges. BISI is actively
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environments, as well as chemically and biologically active phases. The goal of the project is to gain fundamental insight into how the structure of these complex materials evolves under working conditions. To
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. You have a good knowledge of Python and machine learning. You have an excellent knowledge of English. Your research qualities are in line with the faculty and university research policies . You act with
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-type specific samples, state-of-the-art molecular biology techniques, multimodal data generation and integration, gene regulatory network reconstruction and wide range of machine learning approaches
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nanotubes. The most efficient catalysts will be characterized ex situ by electron microscopy and x-ray photoelectron spectroscopy, and under operating conditions by X-ray absorption spectroscopy (XANES and
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/ability, gender, social status, religion and medicine. A close reading of a representative selection of early modern ego-documents (the period and regional focus will be determined by the PhD student
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interest to acquire those in exercise physiology, metabolic & inflammatory regulation, molecular biology and biochemistry. Experience with basic molecular biology techniques, such as protein/mRNA extraction
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the time you start work. You can demonstrate excellent study results. You have a strong background, experience and/or interest to acquire those in microbiology, infectious diseases, molecular biology
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margins in breast cancer surgery, based on a novel Raman spectroscopic technology (IOARM-Raman) that will fit in the surgical work flow. To determine the potential improvement of resection margin status in