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Assistant Professor Position in Transient Electromagnetic Signal Processing, Modelling and Inversion
first ultra-lightweight drone transient electromagnetic (TEM) system for high-resolution subsurface mapping. The project combines novel instrumentation, advanced signal processing, real-time physics
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skills in quantitative acoustics and signal processing. A PhD degree and relevant experience within the specified areas will be considered an advantage. You must have documented experience with
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parallel project carried by another postdoc, which is focused on similar investigation on brain cells in donated postmortem brain tissue. You will be employed in the project Evaluating STING
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, reliability, cost-effectiveness, and sustainability across applications. In parallel, we advance microgrid technologies, supporting developing countries in achieving climate neutrality by integrating
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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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upgrades on existing HTL-WO pilot plant chemical analysis of HTL products, data processing and analysis from experimental campaigns to produce scientific publications and reports active participation in
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, analytical measurements, data processing, and collaboration with researchers and project partners across the value chain. We are looking for a candidate with a background in biotechnology, environmental
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, alternative fragmentation techniques, novel sample processing technologies, or integrated analytical workflows. Expertise in one of these areas is sufficient. Applicants are not expected to possess
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, working with quarto documents, visualizing data, and writing clean & reproducible code) Experience with natural language processing tasks (in R or Python - familiarity with common tasks such as sentiment
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-genome sequencing and resequencing data from non-model arthropods, particularly insects and spiders. A substantial part of the work will involve processing and analyzing genomic data from historical