11 structures-"https:"-"https:"-"https:"-"Lund-University" Postdoctoral positions 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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than 1,800 employees and nearly 21,600 students. The Department of Civil, Environmental and Natural Resources Engineering (CENE) offers unique, collective research expertise in civil and structural engineering
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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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original research in the area of AI-based program construction or performance engineering, co-supervise one or more doctoral students, teach in the first- and second-cycle software engineering programs
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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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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 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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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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fields. Important personal skills involve the ability to collaborate, communicate clearly in an interdisciplinary environment, and work in a structured and proactive way. Awareness of diversity and equal
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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