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Field
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of the correctness of software and hardware systems using machine learning. Recent advances in neural certificates—such as neural model checking and neural termination analysis—have shown promising results in
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. Providing false statements or engaging in acts of plagiarism by the candidates, will lead to immediate disqualification without prejudice to the adoption of other measures of a sanctioning nature. TYPE AND
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of the Fellowship is to develop deeper metallurgical understanding via characterization and modelling to underpin the design of alloys with smaller societal footprints. Three roles are on offer to address
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. The successful candidate will answer questions such as how to assign limited communication resources to train the federated machine learning model efficiently. She/he will investigate realistic scenarios including
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inorganic materials representations, generative design framework, and high-throughput simulations including phonon calculations and dynamic stability, all the way to calculation of phonon thermal conductivity
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of Glasgow/Ifakara Health Institute) to develop and test new methods for trial design and analysis. Developing statistical models and code to run the proposed methods. Developing and running simulation-based
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knowledge in modeling light propagation in non-Hermitian random media. Proficiency in programming languages such as Matlab, Mathematica, or Python. Proficiency in using electromagnetic simulation software
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flood events. The candidate will apply (glacier-)hydrological models to simulate glacier-/snow- mass balance, runoff and extreme events from the past centuries (~1500) to the end of this century, using
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pipelines Expertise in multi-omics data integration and statistical modelling Teaching and mentorship experience Excellent interpersonal and communication skills across wet-lab, computational and facility
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(provided by the group performing experiments) to improve the existing combustion models for unsteady CFD simulations of the burner operating with hydrogen, methane, and their blends for the prediction