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usage, memory and storage demands, and associated carbon emissions while aiming to maintain model quality. Your work will include developing new methodologies and algorithms for resource-efficient
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(glycans) known as the glycocalyx, which is essential for multicellular life. Glycocalyces accomplish critical functions in inter-cellular communication, controlling tissue development, homeostasis and
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critical functions in inter-cellular communication, controlling tissue development, homeostasis and repair, inflammatory and immune responses, neuronal connectivity, and symbiosis with bacteria. However
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myocardial infarction or stroke. To better understand the development and progression of vascular disease, over the past decades we have, together with the Vascular Surgery Clinic, Karolinska Hospital
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learning, with a particular focus on differential equation-driven frameworks. The research will be fundamentally oriented, and the overall mission is to develop computationally efficient and statistically
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application! Your work assignments We are looking for a PhD student to work on the development of novel spatio-temporal machine learning methods. Our world is inherently spatio-temporal, i.e. physical processes
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transformations. The project investigates a hybrid approach that combines deep learning with grammatical inference to develop models that are interpretable, efficient, and mathematically verifiable while leveraging
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algorithms and methods for calibrated Bayesian federated learning for trustworthy collaborative Bayesian learning on data from multiple participants. The project will develop new methods, theory, and
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to continuation as a researcher at Ericsson Research. Practical work tasks include: Developing algorithms and models for dynamic spectrum sharing using RDT data Implementing and evaluating signal processing and
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. Project description This PhD project focuses on advancing the scientific computing foundations of quantum spin dynamics by developing efficient numerical algorithms for modeling complex, open quantum