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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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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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(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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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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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
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medicine and diagnostics, epidemiology and biology of infection. For more information, please see https://www.scilifelab.se/data-driven/ddls-research-school/ The future of life science is data-driven. Will