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to the advancement of precision medicine in oncology. A typical workday may involve writing and running code to pre-process sequencing data on a compute server, applying statistical models and algorithms to construct
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monitoring strategies, and how these aspects affect the co-evolution of code and data pipelines. The research aims to develop empirical insights and practices that enable more sustainable, observable, and
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solid experience in coding with R, analysis of metabolomics and proteomics data, as well as in machine learning. You also need to have good knowledge of magnetic resonance spectroscopy and multiple
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experiments to evaluate protein self-assembly. An ability to derive equations and writing code to analyze data on protein self-assembly. Practical experience in measuring protein size distributions and
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structures, etc to solve challenging problems is required (there will be a practical coding assessment during recruitment) A solid mathematical foundation is required (multivariable calculus, linear algebra
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code for the Eclipse Arrowhead. Projects typically involve working with both academic and industrial partners in Europe. Hence, collaboration with the software and automation industry in the form of both
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automation architecture and the use of AI and Machine Learning (ML). Targeted results include academic papers and contributions with open-source code for the Eclipse Arrowhead. Projects typically involve
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automation architecture and the use of AI and Machine Learning (ML). Targeted results include academic papers and contributions with open-source code for the Eclipse Arrowhead. Projects typically involve
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into consideration. We are looking for someone with a background in environmental governance or a related field, and documented scientific skills in: coding of policies and instruments. survey methodology within
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, machine learning, molecular biology, or a related field is required. If not yet completed, please provide the expected completion date. A computational background and the ability to code independently in