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Field
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optimization of batteries against the swelling phenomenon. This project aims at developing scientific machine learning approaches based on the Bayesian paradigm and electrochemical-thermomechanical models in
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of the research project(s), including design, fabrication, characterization, and modeling of metamaterial fibers and textiles. Develop and optimize fabrication processes for fibers and textiles. Conduct optical
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and sequencing Illumina and/or Nanopore sequencing libraries, or implementing and optimizing Ribo-Seq or ribosome profiling, or analyzing large-scale genomic data (e.g., entire genomes and
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through advanced modelling and simulation. A key objective is to validate and optimize poroelastic finite element models of brain tissue, making them more accurate and clinically relevant. Additionally
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optimal operating conditions and followed by surface analysis techniques (e.g. Scanning electron microscope, X-ray diffraction for residual stress measurements, Electron Back-Scattered Diffraction and
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in a more accurate analysis of optimizing the service performance. Computer vision approaches such as ones for object identification and action recognition can help to automatically identify deviations
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to developing novel computational methods for design and optimization problems in turbomachinery with strong support from Rolls Royce plc. The student will be expected to closely work with Rolls Royce Engineer
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of probability, statistics and optimization. * Proven expertise in the implementation and testing of algorithms. * Strong programming skills in R or Python. * Familiarity with data science and visualization
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on an individualized-level and development of methods to handle high intra-disease and inter-subject heterogeneity. The successful applicant will carry out a wide range of duties including developing, optimizing and
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) applications in the power grid. • Experience with power system modeling, simulation, dynamics and/or optimization, phasor and electromagnetic transient (EMT)-based modeling, and Python and/or Matlab