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of farm animals and to develop the genomic breeding methods of the future. The position involves doctoral studies where you will analyze genomic data, develop new models and simulations, and present new
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processes with more realism in the project. The post will involve global modeling with this new climate model at Lund to investigate mechanisms for the effect from ice nucleating aerosols on clouds and
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, listing any potential publications, and names and contact details of two references Copy of diploma, as well as other relevant certificates such as English proficiency certificate if applicable To qualify
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to submit an attested copy of their passport’s information page containing their photograph and personal details. Read about the PhD education at SLU at https://www.slu.se/en/study/programmes-courses/doctoral
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to, or copy of master thesis, 3) a written motivation or reflection, in English. Place of work: Alnarp Forms for funding or employment: Employment as PhD student (4 years) Starting date: According to agreement
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research group includes both clinical and pre-clinical researchers. Clinical, epidemiological and laboratory-based research is carried out by senior researchers, post-docs and PhD-students. The research is
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-cycle studies at a university or higher education institution. Applicants who are not Swedish citizens need to submit an attested copy of their passport’s information page containing their photograph and
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to submit an attested copy of their passport’s information page containing their photograph and personal details. Read about the PhD education at SLU at https://www.slu.se/en/study/programmes-courses/doctoral
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describe yourself, why you want to do a PhD and why you are suitable for this position. CV (max 2 pages) A certified copy of your master's degree and your course grades. Copies (or drafts) of your master's
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weather forecasting to cardiovascular medicine. Computational tools for simulating such processes - both traditional based e.g. on computational fluid dynamics and more recent based on AI/machine learning