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PhD Student in Mining and Rock Engineering with special focus on underground hydrogen storage Lulea Reference number 4286-2026 Are you ready to take the next step in your academic journey? As a
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affected by AMR pathogens. This will be achieved by integrating cutting-edge techniques in bacterial genomics, including both short-(Illumina) and long-read sequencing (Oxford Nanopore), data mining of
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their application technologies, recycling and reuse, and applied building physics, especially focusing on the Arctic conditions. Project description This research explores how mining waste, such as
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sequencing (Oxford Nanopore), data mining of electronic medical records and use of machine learning to predict several outcomes. Among the approaches used will be the Bayesian information-theoretic
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sequencing (Oxford Nanopore), data mining of electronic medical records and use of machine learning to predict several outcomes. Among the approaches used will be the Bayesian information-theoretic
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cyclic liner tests Building the measurement chain from displacement sensors, pressure measurement, data acquisition to running the supporting laboratory tests Assessing the levelised cost of hydrogen
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motivated candidate with a strong background in molecular biology, human genetics/genomics, data mining, machine learning, and computational programming. The ideal candidate will hold a Master's degree
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Planning and running the in-situ uplift experiments and cyclic liner tests Building the measurement chain from displacement sensors, pressure measurement, data acquisition to running the supporting
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, calcination of limestone, and the negative effects of mining operations. Several solutions are being implemented worldwide, but none have provided an efficient solution. This project aims to study the
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multi-omics data, including WGS, WES, DNA methylation, RNA-seq, proteomics, lipidomics and metabolomics in large-scale human populations. The lab is interested in integrating and mining the different