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prediction to process optimization. The focus of this PhD project is to develop and apply machine learning methods across three interconnected tasks: 3D microstructure characterisation. The student will
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to work at Uppsala University. Duties The PhD student will carry out research in signal processing and machine learning with a strong emphasis on theoretical foundations. The PhD student will actively
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are essential Additional qualifications Experience and courses in one or more subjects are valued: statistical machine learning, optimization, deep learning and signal processing. Rules governing PhD students
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their PhD. Project description The aim of this project is to deepen the fundamental understanding of machine learning through the lens of optimal transport theory, systems theory, and statistical physics
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algorithms. Our research integrates expertise from machine learning, optimization, control theory, and applied mathematics, spanning diverse application domains such as medicine, energy systems, biomedical
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‑mining and machine‑learning methods. The expected scientific outcome is to establish guidelines for identifying and optimizing promising electrolyte materials and to support the development of future
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(1–4) and in related projects. We encourage potential PhD candidates to visit our webpage to learn more about the research we are conducting. The PhD candidate is expected to be enrolled in two
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to machine learning is well funded and continuously publishes in high impact journals. We foster a creative working environment, where you will find freedom to implement, develop, and publish research
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Python) and data analysis or machine learning applied to materials science Ability to work in interdisciplinary project or industrial experience About the employment The employment is a temporary position