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application! At the intersection between AI and single atoms. Your work assignments We are looking for a PhD student with a background in machine and deep learning with focus on image processing and restoration
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multiphase flow behavior. The project also involves applying machine learning and computer vision techniques to enhance data analysis, pattern recognition, modeling, and prediction. The role requires a solid
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theoretical analysis, implementation of methods in computer codes, use of state-of-the-art high-performance computers in Sweden and in Europe, application of machine-learning and AI techniques, and
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northern Europe. Our research covers a broad spectrum of fields, from core to applied computer sciences. Its vast scope also benefits our undergraduate and graduate programmes, and we now teach courses in
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Chemistry (experimental/computational physical chemistry) -Transition metal photocatalysts studied by femtosecond X-ray science with a focus on hybrid experimental/machine-learned structural dynamic analyses
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. Conducting most of the development in a digital environment is particularly important when dealing with mobile, heavy and powerful machines, and especially in the early development phases when they exist only
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. Conducting most of the development in a digital environment is particularly important when dealing with mobile, heavy and powerful machines, and especially in the early development phases when they exist only
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of visualization and multimodal machine learning. Admission requirements The general admission requirements for doctoral studies are a second- cycle level degree, or completed course requirements of at least 240
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to assimilate knowledge at the research level. Understanding and experience in machine learning and computer vision. Knowledge, experience, and strong interest and in AI and XR development. Knowledge and
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multimodal machine learning. Admission requirements The general admission requirements for doctoral studies are a second- cycle level degree, or completed course requirements of at least 240 ECTS credits