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Seed project: Investigate Machine Learning Techniques for Decision-Making Support in K-12 Educational Context The main aim of this seed application is to investigate the use and application of Machine…
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Project: In-line visual inspection using unsupervised learning The purpose of this project is to introduce and improve machine learning- assessment of the quality of massproduced industrial (steel)…
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Seed project: Using Natural Language Models for Extracting Drug-Related Problems (NLMED) The overall goal of the research in this seed project within the Linnaeus University Center for Data Intensive…
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Project: HPC for SME The aim of the project is to provide small and medium-sized enterprises (SMEs) in the Linnaeus region with the opportunity to enhance their data-driven capabilities with the…
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Seed project: Using Artificial Intelligence to Detect Acanthamoeba Keratitis in the eye - the AIDAK study Applicants The overall objective of the research for this seed project within Linnaeus…
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Seed project: Towards a data-driven approach to ground-fault location The main objective for this seed project within Linnaeus University Centre for Data Intensive Sciences and Applications (DISA) is…
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Project: Using past data to prevent future fractures Certain types of medication increase the risk of osteoporosis and fractures. In this research project, we develop a risk assessment tool which can…
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Seed project: Machine learning stabilized steady-state advective-diffusive heat transport This seed project aims to explore and use the strengths of Scientific Machine Learning (SciML) to solve the…