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experience in population genomic modeling (e.g., using SLiM), analysis of structral variants from long‑read data, population genomic analysis of whole‑genome re-sequencing data are a merit — these techniques
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campus in Luleå, Skellefteå and we are also responsible for the research at Green Fuels in Piteå. Subject description Machine Elements comprises the analysis and optimisation of machine components and
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of biomass conversion technologies. Experience in programming with Python. Experience in Life Cycle Assessment. Experience in techno-economic analysis. Experience in scientific writing and communication
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communication skills in Swedish, Strong knowledge of electric power engineering, power electronics, and power system analysis, Experience of modelling, simulation, and experimental work. In an overall assessment
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system analysis. The work includes integrating AI methods with energy system models as well as developing methods for transparency, explainability, and uncertainty management, with a particular focus on
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geological materials and organics; knowledge of spectroscopy and/or scanning and/or transmission electron microscopy techniques. Merits are: experience in spectral data analysis using Matlab, R or Python; a
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precision medicine and diagnostics covers data integration, analysis, visualization, and data interpretation for patient stratification, discovery of biomarkers for disease risks, diagnosis, drug response and
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(see below) are also encouraged to apply. Proficient in at least one programming language, preferably Python or R. Experience in any of the following areas: large scale sequence analysis, microbial
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approach, allowing for a range of suitable methods for data collection and analysis. The doctoral project is initially funded by the Jan Wallander and Tom Hedelius Foundation and the Tore Browaldh Foundation