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Assistant Professor Position in Transient Electromagnetic Signal Processing, Modelling and Inversion
suppression, and data compression techniques for TEM systems. • Integration of numerical methods into real-time drone-based TEM acquisition workflows. • Participation in the design, testing, and
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the Department of Electrical and Computer Engineering, Aarhus University, where we are advancing communication-efficient and distributed foundation model inference across the computing continuum
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and energy-management methods for coordinating electrolyzers with renewable generation, power conversion, storage, downstream processes, and hydrogen demand. Apply and evaluate these methods under
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. This is a fixed-term position to end 24 months after the start date. Job description The postdoc position focuses on physics-driven machine learning methods for ground-based, airborne and drone-based
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-noise) data. Develop and validate numerical implementations on simulated and real CT data. Explore and compare supervised and self-supervised machine learning/AI-based reconstruction methods with
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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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Two postdoc positions in Thermofluid Design and Digital Modeling of Cooling Systems for Electroch...
parallel computing. Familiarity with digital twins and real-time deployment of predictive models is an advantage. Who we are The two postdoctoral positions will be hosted across the two complementary
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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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with Poleno Jupiter instruments, with data obtained with DNA methods and data obtained with a Hirst trap. • Numerical models used by the group covers the particle dispersion model HYSPLIT in
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process modelling, numerical methods for uncertainty quantification and rare event estimation. Scientific computing, possibly with different programming languages (e.g., Python, Julia, C#, MATLAB