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internal reports and manuscripts. Requirements: PhD in Physics, Materials Science, Computational Science/Engineering, Computer Science, or related. Solid knowledge of machine learning, including graph neural
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optimisation, uncertainty/robustness analysis, and surrogate-assisted search (e.g., simple regressors, Gaussian processes, or BO). Prior work with workflow managers (e.g., AiiDA/Airflow/Snakemake) and
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documentation. Contribution to applications to HPC centres (with a focus on EuroHPC machines) in order to secure the resources needed for architecture-dependent code development, optimised deployment and
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fields. Main tasks and responsibilities: · Operate and optimize advanced STEM and FIB instrumentation for the nanoscale analysis of catalytic and energy-related nanomaterials, supporting the development
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and 2D materials, and oxides. Fabrication of functionalized Pb-free MHPs by solution processing methods. Fabrication of complete Pb-free MHPs solar cells and memristors (TFTs). Stability analysis
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detailed knowledge of main physical principles, concepts, and applications of electron microscopy. Advanced sample preparation (FIB) and data analysis skills. Excellent interpersonal skills in order to
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critical thinking, experimental design, data analysis, and troubleshooting, with a proactive approach to overcoming scientific and technical challenges. Proactive and self-Motivated: Demonstrates initiative
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critical thinking, experimental design, data analysis, and troubleshooting, with a proactive approach to overcoming scientific and technical challenges. Proactive and self-Motivated: Demonstrates initiative
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processes. Performing benchmarks in HPC machines in Japan and carrying optimizations to improve performance of the codes. Execution of simulations in top HPC facilities, both in Europe and Japan. Contribution