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. Project overview The project involves applying advanced statistical analysis, machine learning techniques, and modeling approaches such as agent-based modeling to analyze diverse climate and socioeconomic
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environment project, we will develop automated species and community recognition, particularly focusing on pathogenic soil fungi, with help of deep-learning algorithms fed with microscopic image and Raman
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. Requirements PhD degree in biochemistry or structural biology, or an exam which is judged comparable to a Swedish Ph.D in biochemistry or structural biology. The degree needs to be obtained by the time of
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of an excellent team of several PhD students, PostDocs, and Researchers working on different projects related to biotechnological methods for producing recombinant silk proteins, characterization of these, spinning
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facilities that are highly aligned with the goals of the project. Who we are looking for We seek candidates with the following qualifications: To qualify for the position of postdoc, you must have a PhD degree
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forests and marine environment and pest surveillance in aquafarming. Our group will comprise a handful of PhD candidates, and several researchers and MSc students and also a broad interdisciplinary network
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limit can be made for longer periods resulting from parental leave, sick leave or military service. The following experience will strengthen your application: Experience with synchrotron-based
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application! Work assignments Subject area: Computational studies of the influence of microstructural features on the structural integrity of metallic materials using machine learning Subject area description
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The postdoc fellow will conduct research in the intersection of AI/Machine Learning and Software Technology. The advertised position will be placed in the DISTA research group (https://lnu.se/en/dista
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to the application deadline. PhD in computer science, electrical engineering, biomedical engineering, or a related field. Experience in Python programming, natural language processing, and multimodal deep learning