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. The HEXAPIC project aims to develop a novel high-performance Particle-In-Cell (PIC) code for plasma physics simulations, leveraging the capabilities of exascale computing systems. By optimizing PIC algorithms
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CO2 capture from the atmosphere. Your objectives will include to: Develop new optimization and/or machine-learning based reconstruction and segmentation algorithms to improve image quality in time
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different optimization methods using low rank tensor minimization and tensor decompositions paired with auxiliary information in order to recover missing links in a multilayer network with connected
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algorithms and codes for AI-enabled digital twin technologies. Design advanced numerical algorithms for partial differential equations and optimization problems related to digital twin technology. Implement
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for tens of kilometers length. In this project, you as a postdoctoral researcher will mainly carry out: optimization of both fiber preform preparation and fiber drawing processes so as to reliably fabricate
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requires not only expertise in LLMs and machine learning but also an understanding of the unique challenges posed by scientific data, which often includes large-scale numerical datasets, complex simulations
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to produce optimal designs. Applicants should have skills in modelling, familiarity with partial differential equations, and be familiar with python. They will have, or be close to completing, a PhD in
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Systems will participate in the research efforts of developing systems integration, analysis, design, control, and/or optimization models and algorithms for smart energy systems to enable smart and healthy
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possible until 31 December 2027. Key responsibilities and duties: Use analytical and numerical mathematical tools to design a sensor system using infrasound and ultrasound. Propose different sensor systems
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in adaptive immune systems (e.g., co-evolution of bacteria and phages, as well as T and B cells with pathogens). • Physics-informed machine learning of biophysical systems (e.g., developing optimal