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considered an advantage if you have: Experience with protein language models (e.g., ESM, ProtT5) Experience with structure prediction frameworks Experience in geometric deep learning or graph neural networks
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considered an advantage if you have: Experience with protein language models (e.g., ESM, ProtT5) Experience with structure prediction frameworks Experience in geometric deep learning or graph neural networks
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The Department of Biomedicine at Faculty of Health at Aarhus University invites applications for a position as Postdoc in the field of Transcriptomic analysis of the immune response in Parkinson’s
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., compressive sensing, wavelet transforms; adaptive filtering & interference rejection, e.g., Kalman filter, notch filter, spatial filtering; 4) time-frequency analysis & spetrual estimation, e.g. STFT
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project activities. Key responsibilities include: Investigation into the GEUS sediment archive to extract information on the properties of subglacial sediments deposited by past ice streams. Analysis
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studies and both qualitative (e.g. interviews or ethnographic methods) and quantitative data analysis. This postdoc is an opportunity to collaborate in an international Nordic project with external partners
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botanical journals, as well as Johan Lange’s role as editor and populariser of the Flora Danica project. The postdoc will identify and examine episodes of specimen collecting, botanical collaboration, visual
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biochemistry. Expertise in e.g. CRISPR-mediated gene editing, protein structure prediction, mass spectrometry-based proteomics, or fluorescence microscopy. Expertise and interest in functional analysis
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advanced properties, primarily concerning circular RNAs. The postdoc project will be involved in all aspects of COL but focuses primarily on visualizing and structural characterizing catalytic RNA complexes
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Postdoc position in method development in human statistical genetics, with a focus on classificat...
., biomarkers and traits related to the target disease). There is also scope within the postdoc for developing “more standard” GWAS tools (e.g., tools for association analysis, constructing polygenic scores and