257 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"L2CM" positions in Switzerland
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motivation letter the names of three referees Further information about the Earth Surface Geochemistry group can be found on our website . If you have any questions regarding the position, you are welcome
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. Profile We highly encourage candidates with the following skills to apply: Background in Plant Biology / General Biology Fluency with English Experience with bioinformatics (analysing genomics data) using R
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AI initiative, aimed at bolstering our leader- ship in AI and Deep Learning. The role will significantly enhance the capabilities of our Center for Data Analytics and support interdisciplinary research
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. All data is acquired and processed using SeisComP. Project background We are seeking an outstanding scientist with strong interest in network seismology and software development to enhance our
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systems and special emphasis on data centers and cloud computing. Within these areas, topics of interest include protocol design, performance analysis, system architecture, distributed applications at scale
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order to promote human health and chemical and drug safety. Researchers develop and apply quantitative chemical, in vitro, and computational approaches to address problems in Toxicology. More information
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your motivation to apply for this position Grade transcripts of your MSc and BSc studies Contact information for two academic references There is no deadline for applications. We are constantly reviewing
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to extract key structural information that goes beyond enantiomer discrimination. You will work on both developing nanophotonic systems that enhance light chiral matter interactions and novel imaging systems
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graphic designer to prepare graphics for individual analytical chapters. This will include identifying suitable sources and databases; collecting data for infographics and preparing them for design; and
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position (technician) will focus on performing Raman/FTIR on retrieved samples. The PhD position will focus on developing a deep-learning algorithm for analyzing the acquired experimental data.