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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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dynamics. Particular emphasis is placed on opinion dynamics as well as distributed problems in coordination, optimization, and learning. The research encompasses both theoretical and computational aspects
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. Your work may also include teaching or other departmental duties, up to a maximum of 20 per cent of full-time. Your qualifications You have graduated at Master’s level in computer science, mechatronics
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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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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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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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, 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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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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6 Mar 2026 Job Information Organisation/Company Linköping University Research Field Computer science Researcher Profile First Stage Researcher (R1) Application Deadline 24 Apr 2026 - 12:00 (UTC
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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