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Systems (CUS), which promotes resilience, sustainability, and performance in urban and territorial systems. The researcher will focus on the design and application of hydrological, and AI-based modelling
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, to decipher the early mechanisms of tumor transformation. We will leverage innovative models to investigate the early stages of tumor transformationin the context of brain development. Specifically, we will use
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and automation of lab equipment using Labview or similar, TCAD simulation and physics-based modelling and cleanroom device processing. A willingness to work together with, and co-supervise, PhD students
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studies, that are aligned with mentor teams research, ensuring consistency with established methodological approaches and models, adherence to project timelines, and completeness of documentation; write and
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focused on exploration and development of AI models of auditory perception, towards a broader goal of understanding how the brain predicts and learns from human communication sounds such as speech and music
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and field monitoring work performed by a PhD student at LIST and other researchers in LAFI, and extend the existing Vegetation Optimality Model (VOM, https://vom.readthedocs.io ) to test the following
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. The project will develop methods and approaches using novel polymerisation to produce 3D porous materials to enable 3D cell growth and bioengineered in vitro models. The successful candidate will join the
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, and systems-level approaches with advanced model systems—including organoids and in vivo disease models—to investigate mechanisms regulating epithelial homeostasis, host–pathogen interactions, immune
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Commensurate with experience Type of Position Student Position Time Status Full-Time Required Education PhD Required Related Experience Experience in life science or related disciplines. Required License
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Diogo Arsénio on subjects including: Magnetohydrodynamics and the mathematical theory of plasmas. Collisional kinetic models and their hydrodynamic limits. Dispersive phenomena in kinetic theory. Quantum