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in our large and diverse academic School of Biosciences. As this role requires a combination of state-of-the-art approaches, it provides an excellent opportunity to develop your skills in
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element models. ● Work collaboratively with other colleagues for the validation of the finite element models of rib samples based on in situ mechanical testing data. ● Lead the model uncertainty
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year round Details Proteins bind and recognise each other using large surface areas. This recognition process is vital for a variety of biological applications. Sensing and recognizing specific proteins
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data gaps by combining process simulation (e.g., Aspen software) with machine learning techniques. By developing accurate, large-scale life cycle inventory data using enhanced digital tools like deep
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collisions at an unprecedented energy of 13.6 TeV, delivering the largest amount of available data yet. The ATLAS experiment itself is a general purpose detector that allows to study a large range of particle
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accepted all year round Details DUNE is a large international project to design, construct and operate a multi-kilotonne scale liquid argon detector for neutrino physics, neutrino astrophysics and a search
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development in modern cements formulated with SCMs is therefore urgently required, to enable quality control and make them practical for use in large-scale construction. This PhD uses advanced spectroscopic
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technologies, including: Smart Shop-Floor Connectivity 5G Factory of the Future Big Data Artificial Intelligence (AI) Automation and Robotics for advanced manufacturing systems. Candidates should have strong
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system will expect to require 25-45GW of within-day energy flexibility, a large proportion of which will need to be delivered by battery EVs. This flexibility can take the form of dynamically changing
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via traditional analytical methods extremely challenging. This project will apply pattern recognition and machine learning techniques to a large database of experimental data to reveal early-stage