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the human gut microbiome are poorly characterized at their molecular level, despite their important roles to keep a healthy state. The presence of pathogenic species or imbalances in the composition
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. The project revolves around developing Traident – a new method to resolve the species origins and compositions of complex RNA sequence data. This will extend Kraken2 with analyses of ribosomal RNA and microRNA
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novel sustainable materials. This PhD project is a great opportunity to pursue pioneering research on the scale-up of circular, biobased material solutions for architectural applications in sustainable
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variability in risk factor susceptibility, treatment response, disease pathogenesis, and clinical diagnosis (biostatistics, machine/deep learning), ii) Investigating causal processes and disease mechanisms
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the driver to actively participate and accept the interventions. A key component in the project will be to practically test feasibility and assess the performance of algorithms and hypotheses in test track
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. Position description Construction industry is one of the biggest contributors to waste and carbon emission and this sector is at a crossroads. Unless major changes, the industry risks failing to meet the
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. Of particular interest is the investigation of compositional methods for constructing runtime monitors. The candidate will build on the latest advances in formal methods and learning theory, to develop methods
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closely with a co-supervisor at the Division of Material and Computational Mechanics. The NEST-WISE project offers a vibrant collaborative environment and close interaction with academic and industrial
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This PhD position is part of the WASP-WISE NEST project RAM³ – a multidisciplinary research effort at the intersection of machine learning and materials science. The project brings together PhD
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future product owners and users is a key factor to the success of these projects. In this, you will work closely with other LTU researchers and doctoral students. Duties As a PhD student, you work towards