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Field
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Development and training of AI/machine learning models Real-time validation of algorithms to ensure reliable physiological monitoring under real-world conditions The role offers the opportunity to contribute
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exhibit hallmarks of active matter. This PhD project aims to develop theoretical and computational active-matter models of early mouse embryogenesis that couple collective cell mechanics with gene
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analysis, PDE-constrained optimization and optimal control, numerical analysis and scientific computing risk-averse and fractional models, digital twins and data-assimilating models, machine learning and
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to developing new models, techniques and methods, relating to early executive functions, motor skills, and parent-child play Undertake management/administration arising from research Contribute
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complex materials systems. Advanced data analysis and scientific model development using Python or other scientific programming languages, including experience with automation, instrumentation control
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and cancer biology. A broad set of molecular biology skills. Some Bioinformatics training would be an advantage as would sophisticated flow cytometry experience and tissue culture/animal models
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statistical techniques (e.g., multilevel modeling, SEM) or qualitative analysis (e.g., grounded theory, thematic analysis). Experience with grant writing or collaborative team science. Physical Requirements
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to develop theoretical and computational active-matter models of early mouse embryogenesis that couple collective cell mechanics with gene regulation. The goal is to identify the physical and mechanochemical
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and granular substrates. In this role, you will perform numerical simulation and reduced-order modeling of naturally-shaped granular materials interacting with specific foot structures. You will also
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characterization - Experience with data modeling and programming (MATLAB required) - Expertise in semiconductor manufacturing processes and device failure analysis - Experience working in cleanrooms - Ability