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conduct research on the theoretical foundations of mathematical optimization, as well as its applications to emerging challenges in machine learning and engineering. You will write and submit research
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NAISS, the National Academic Infrastructure for Supercomputing in Sweden, provides academic users with high-performance computing resources, storage capacity, and data services. NAISS is hosted by
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. Such methods are founded on mathematical models and algorithms from the field of mathematical optimization. The dose planning is based on medical images and a treatment protocol, and it is evaluated by using key
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precision medicine based on gene sequencing time series data. Large data sets come with significant computational challenges. Tremendous algorithmic progress has been made in machine learning and related
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distributed computational pipelines and optimizing communication costs. You will also contribute to the integration and testing of the models in real D-MIMO environments, in close collaboration with a PhD
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existing and creating new deep learning-based models for anomaly detection, theoretical and numerical studies of detection quality, creating new distributed computational pipelines and optimizing
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, and datasets; often at substantial computational and environmental costs. This PhD project targets sustainable and resource-efficient machine learning with a focus on methods that reduce compute, energy
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=sv The employment When taking up the post, you will be admitted to the program for doctoral studies. More information about the doctoral studies at each faculty is available at https://liu.se/en
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communication limitations, adversarial conditions, continual and adaptive learning in dynamic environments. The research will combine tools from distributed optimization, stochastic approximation, information
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application! We are looking for a PhD student in Statistics and Machine Learning Your work assignments We are looking for a PhD candidate to work in the intersection of computational statistics and machine