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science and philosophy. The REMAX project on Rigorous Evaluation Methods for AI Explainability, funded by a Villum Synergy grant, aims to create methods for explainable AI and their rigorous evaluation
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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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. 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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-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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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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physics and experience with quantum many-body scars is an advantage. Experience within numerical methods for quantum many-body systems is required. Personal and collaborative skills are also important
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approaches 3. Apply these methods across different crops to identify conserved patterns of stress resilience 4. Identify candidate genes associated with key agronomic traits related to resilience 5
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multicomponent inorganic energy materials. Use molecular dynamics, lattice-dynamical methods and statistical sampling to investigate local disorder, phase stability, temperature-dependent behaviour and
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Experience with programming in Python, R, or similar languages Background in machine learning or deep learning methods Knowledge of genomics, transcriptomics, evolutionary biology, and plant biology is an