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experience designing and deploying automation and control architectures that span from edge devices (e.g. grid-forming converters, IoT controllers) to cloud-based platforms. Practical knowledge of distributed
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optimizing PIC algorithms for modern heterogeneous architectures, including CPUs, GPUs, and other accelerators, the project seeks to achieve unprecedented efficiency and resolution in plasma simulations
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optimizing PIC algorithms for modern heterogeneous architectures, including CPUs, GPUs, and other accelerators, the project seeks to achieve unprecedented efficiency and resolution in plasma simulations
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will be developing control electronics based on Arduino or Raspberry Pi and write the corresponding software, ensuring significant automation and simple user interfaces such that other researchers can
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advanced grid-connected hardware architectures. · Implementing control loops for grid-connected power converters. · Developing gird-following and grid-forming functionalities
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context (i.e., different data source from human to technical, from knowledge to generative AI models). Collaborate with software engineers to integrate the management infrastructure within the Digital Twin
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publications. Implement and open-source proof-of-concept software tools. Collaborate with other researchers of different projects and disseminate the outputs at conferences, workshops organized by the consortium
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: Extensive experience in machine learning methods, tools, and platforms. Proficiency in Python, with demonstrated software development experience. Hands-on experience in MLOps, including the design and
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analysis or machine learning. The candidate will conduct integrative analyses of biomedical datasets, with a focus on omics data. This will include implementing and applying software analysis pipelines and
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, multi-level, discrete choice behavior modeling…) based on statistical software (R, STATA or SAS) ;Excellent skills in GIS and Python are an asset ;Excellent communication and collaboration skills