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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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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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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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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
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into that framework, and to take advantage of these tools to produce optimal designs. Applicants should have skills in modelling, familiarity with partial differential equations, and be familiar with python. They will