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Field
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optimization of b-tagging performance for the upgraded tracker. Responsibilities: Conduct independent and collaborative research. The successful candidate will be expected to take a leading role in physics
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optimization of multi-modal LLMs. Investigate and implement methodologies to ensure AI authenticity, accountability, and the integrity of digital content. Develop and refine machine learning and deep learning
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machine learning—for chemical and biological applications. You will design and implement models ranging from molecular to process scales, develop model-predictive control and optimization strategies, run
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are gaining significant interest for sustainable seawater desalination. This postdoctoral position is part of a research project aiming to design, optimize, and evaluate electrochemical systems [electrodialysis
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master's degree in agricultural or environmental economics, applied mathematics, or a related fieldGood background in bio-economic modelling, CGE modelling, and optimization techniquesExperience with
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data management, optimizes processing pipelines, learns new analysis techniques or software packages as they become available. Assists with administrative tasks related to studies. Provides training and
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techniques may be employed to support the modeling of uncertainty, along with the formulation and resolution of planning problems using stochastic optimization methods. Key Responsibilities The selected
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to develop and optimize scalable experimental protocols across diverse material families. This role is part of a multidisciplinary team integrating materials chemistry, machine learning, and autonomous
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bottlenecks and optimization strategies. The digital twin will serve as a testbed for evaluating engineering trade-offs and guiding future hardware development. The appointed researcher will collaborate with
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, determining optimal ways for groups of buildings to share resources and benefits. You will investigate and quantify trade-offs between individual objectives and collective outcomes, focusing on scalability