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at the intersection of advanced probabilistic machine learning and microbial bioscience. This position offers a unique opportunity for developing novel probabilistic ML methods with a view towards
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individual processes to entire manufacturing systems. The positions will all focus on factory and line level, where three research topics are defined: 1) Conceptual design principles and methods for resilient
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characterization in complex in-situ environments. The key responsibility of the position is to develop post-processing methods to extra essential features from the collected measurement data despite drone positional
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relating to thermal stress responses. Additionally, the postdoc will also be developing methods for integrating thermal tolerance measures with microclimate data from field sites and climate models to better
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national lab-based testbed on circular manufacturing, creating multiple demonstrators on how technologies aid in operationalizing circular manufacturing. This includes e.g. developing effective methods
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A position focused on development of a novel Time Series Management System (TSMS) is available at the Data Engineering, Science and Systems (DESS) group at the Department of Computer
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clear focus on algebraic coding theory and its applications. Fluency in English is required. Familiarity with computer algebra systems as SageMath or MAGMA is a plus. The candidate should have a strong
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program. EUROSTARS supports innovation for small and medium-sized enterprises(SMEs) being co-funded by Horizon Europe with contributions from Innovation Fund Denmark to fund the Danish collaboration
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mentoring students. Qualification requirements The ideal candidate has: A PhD in bioinformatics, computational biology, biostatistics or metabolomics Proven expertise in omics data analysis A history of high
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and biotechnology. The Center has state of the art equipment for DNA analyses, computing and advanced microscopy. The successful candidates must have documented expertise in microbial(meta)genomics