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Field
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ingredients on human health outcomes. This includes designing, executing, and optimizing in vitro cell culture and microbiological experiments relevant to gut microbiome function, diabetes, lipid dysbiosis, and
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to evaluate emerging instrumentation and software and translate them into robust, scalable workflows. Alongside method development, the Zamboni Lab is tightly embedded in numerous collaborations with academic
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, components, overall system performance) Numerical methods and simulation tools (e.g., Python/Matlab, CFD modelling, optimization) Beyond technical skills, we value people who contribute to a healthy and
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Helical-AI on scaling, optimization, and integration of the developed models within an HPC-enabled industrial pipeline Publish research results in leading international conferences and journals in machine
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well as dissimilar combinations has taken shape with the aid of numerous funded projects implemented in close co-operation with industries. However, there has been very limited previous work globally addressing
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biological applications. You will design and implement models ranging from molecular to process scales, develop model-predictive control and optimization strategies, run high-performance numerical experiments
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; numerical analysis; programming; optimization; stochastic analysis; systems theory. What you will do You are expected to develop your own ideas and communicate scientific results orally as well as in written
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advanced research in structural health monitoring, including hardware development. Perform numerical studies and model testing. Analyze and interpret data to provide recommendations for system design and
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Engineering, and related fields, with a view to develop and optimize 3D bioprinted models, cell selection and expansion for bone models, biological and mechanical characterization of bioprinted tissues
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the BERNARDO project, funded by the PEPR LUMA program (https://www.pepr-luma.fr/ ). The project focuses on chiroptical interactions, which play an essential role in fields such as biomolecular recognition