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As part of the PICs@ICFO Strategic Initiative, ICFO coordinates PIXEurope, a flagship Pilot Line funded under the European Chips JU program. With €400 million in investments and a consortium of 20
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As part of the PICs@ICFO Strategic Initiative, ICFO coordinates PIXEurope, a flagship Pilot Line funded under the European Chips JU program. With €400 million in investments and a consortium of 20
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computing frameworks (e.g., MPI, NCCL) and model parallelism techniques. Proficiency in C++/CUDA programming for GPU acceleration. Experience in optimizing deep learning models for inference (e.g., using
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, with a highly innovative research and technology transfer project, this could be your opportunity. Main Tasks and responsibilities: To develop and optimize the analytical performances of the multipled
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dynamic, uncertain worlds. Multi-Objective & Black-Box Optimization: Real-world problems rarely have a single, simple objective. We research methods to navigate complex trade-offs (e.g., performance vs
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to) SIESTA (www.siesta-project.org) and its TranSIESTA functionality. SIESTA is a multi-purpose first-principles method and program, based on Density Functional Theory, which can be used to describe the atomic
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ESEM, a FIB system, and a complete suite of sample preparation tools. The role will involve the coordination of day-to-day operations of the unit, ensuring optimal equipment performance, user
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. Evaluate and implement digital tools and platforms that support research activities, data management, and collaboration. Process Optimization: Analyze current workflows across research and administrative
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areas of nanoscience and nanotechnology. Job Title: Research Support Technician in Active learning Research area or group: Theoretical and Computational Nanoscience Description of Group/Project: In
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materials technologies. We focus on the unique physical properties and disruptive application potential of two-dimensional materials, leverage intelligent materials design and multi-scale computational