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advantage Familiarity with large biological datasets and high-performance computing is desirable Ability to work independently and collaboratively in an interdisciplinary research environment
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biological data collection, extraction, and standardization Knowledge of multi-omics integration and plant biology is an advantage Familiarity with large biological datasets and high-performance
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platforms geospatial data analysis and use of GIS software (e.g. QGIS) and cloud-based geospatial analysis platforms such as Google Earth Engine statistical modelling, machine learning, cloud/high-performance
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. Strong skills in scientific scripting (R, Python) and programming. Experience with High-Performance Computing (HPC) environments and the management/analysis of large datasets. The ability to
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focusing on: Next-generation energy storage systems Power converter design for Power-to-X (PtX) solutions Microgrid applications Quantum computing in power systems And other emerging technologies in
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high-performance computing, automated workflows and reproducible data analysis; proficiency in Python or a comparable language is expected. A publication record appropriate to career stage that
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-based surrogate models. Experience with high-performance and parallel computing. Familiarity with digital twins and real-time deployment of predictive models is an advantage. Who we are The two
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experience and the opportunity to work across two leading labs in plant genomics and AI. Computational ressources, including the GenomeDK high-performance computing cluster, with growing capabilities such
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ecology, microbiome analyses, shotgun meta-genomics, microbial functional annotation, and high-performance computing are encouraged to apply. Who we are The Department of Ecoscience at Aarhus
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correctness. Valued: Experience with high performance computing (HPC). Prior exposure to mass spectrometry, omics, audio, or other high-dimensional structured data. Interest in open-source software