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Field
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provides a dynamic and inspiring environment for researchers to address the grand challenges of our time. The post doc position is part of the research group “Spatial Data Science and GeoCommunication” led
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for the study of the history of the world. We are an intellectual home for scholars of every region of the world, who use approaches which range from local micro-histories to large-scale quantitative
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candidate must have experience in the following areas: molecular cell biology, plant phenotyping, and image/data analysis, as well as working as part of a large team. A track record of publishing research is
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considered. The successful candidate must have experience in the following areas: molecular cell biology, plant phenotyping, and image/data analysis, as well as working as part of a large team. A track record
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approaches which range from local micro-histories to large-scale quantitative analysis. We particularly value conversation between scholars of different periods and places, with different approaches. We also
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flexible working hours, remote/hybrid and/or part-time work (upon agreement). Inspiring working atmosphere: You are a part of a vibrant department with a big international community of other post docs and
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, handling and processing of large data sets, knowledge of data acquisition systems, signal and image processing as well as experience in measuring acoustic pressures in cavitating liquids and high-speed
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related field together with strong programming skills in Python, R, or similar languages, and proficiency in high-performance computing. You will have experience in large-scale genomic data analysis. You
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(myofiber) handling Experience in handling, processing, analysing and interpreting large data sets Experience in mentoring, leading and organising junior laboratory members and laboratory arrangements (e.g
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to prevent and/or mitigate the impact of E. coli and Klebsiella spp. infections, including those associated with antimicrobial resistance (AMR). As part of this post there is an opportunity to analyse large