13 structures-"https:"-"https:"-"https:"-"LInköpings-universitet" Postdoctoral research jobs in Sweden
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education to enable regions to expand quickly and sustainably. In fact, the future is made here. The Department of Chemistry seeks a postdoctoral researcher who will work in the field of in situ structural
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molecular biology: Computational Biology and Bioinformatics, Microbiology and Immunology, Molecular Biology, Molecular Biophysics, Molecular Evolution, Molecular Systems Biology and Structural Biology. The
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expertise in civil and structural engineering. The research subject Structural Engineering is now seeking a highly motivated postdoctoral researcher contributing towards science and technology in condition
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challenge is therefore to determine whether shared biological structure can be recovered across heterogeneous datasets or whether morphology is dominated by context-specific variation. The MirandaLab is
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excellence (publications, open-source contributions, or comparable output) Desired Qualifications Experience or strong interest in the integration of language models with structured knowledge. Familiarity with
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Subject description The Center for Molecular Protein Science (CMPS), Department of Chemistry brings together scientists active within the fields of biochemistry, molecular biophysics, structural
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Collaborative abilities Independence Well-organized / structured Have a professional approach Analyze and work with complex issues Good knowledge of English and good presentation skills in text as it is required
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to work in a structured and methodical manner Eligibility A person who has been awarded a doctorate in a subject relevant to the position, or a foreign qualification deemed to be the equivalent of a
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to contribute to applications for external research funds. In this project, your responsibilities will include leading the investigation of structural and biophysical properties of miniaturized tumor environment
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loop/TAD structures. - Perform comparative analyses versus Populus tremula; apply network modelling and machine learning for regulatory inference. - Functional validation of candidate TE‑CREs in spruce