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Field
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. Develop AI, machine learning, optimisation, and decision-support algorithms. Build digital twin environments, physics-based modelling, concept evaluation frameworks, and technical risk assessments. Develop
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of experimental protocols, software pipelines, and laboratory procedures • Contribute to data-driven approaches for predictive colloid science, including experimental design, data analysis, modelling, and machine
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Job Description We are seeking a highly motivated Research Fellow to conduct independent and collaborative research in quantitative, statistical, computational, or data-driven research areas
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use in vivo two-photon calcium imaging in mice, alongside behavioural assays and computational modelling, to track how learning-driven changes emerge in these early circuits and how this shapes
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inflammation, immune resolution, and host defense. We are seeking a scientifically independent, driven, and intellectually curious researcher who is motivated to develop new ideas, generate rigorous and
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resilience planning for the Treasure Valley. Unit URL https://iwrri.uidaho.edu/ Position Qualifications Required Experience Experience working with quantitative analysis, modeling, or data-driven methods
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. Specific topics of focus include, but are not limited to, linear response, statistical limit laws, random and nonautonomous dynamical systems, spectral analysis, machine learning, data-driven dynamics
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computational modelling, to track how learning-driven changes emerge in these early circuits and how this shapes perception. You will join a collaborative lab within a thriving neuroscience community at Leeds
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: emerging mobility, mixed traffic modelling, connected and automated vehicles, micromobility, experimental design using driving simulators, virtual reality (VR) and augmented virtual testing, and test tracks
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are produced -- making the process cleaner, smarter, and more efficient. This is an opportunity to help reshape how critical materials are produced -- moving toward smarter, data-driven processes