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factor – DNA binding, detecting protein – protein interactions or enzyme optimization. Main responsibilities The candidate will use and develop methods within one, or preferably multiple, of the following
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information about us, please visit: the Department of Biochemistry and Biophysics . About the DDLS PhD student program Data-driven life science (DDLS) uses data, computational methods and artificial
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or advanced statistical methods, to help explore molecular, imaging, clinical and/or epidemiological data. You will apply, adapt and develop machine learning approaches to provide data analysis support to data
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boundaries creates synergies that drive research forward and the high-tech methods enable research that would otherwise not be possible in Sweden. At SciLifeLab, we do not only apply the latest and most
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chloroplasts. This project will develop and apply new synthetic biology methods with the aim to enhance the rate of carbon fixation and conversion in cyanobacteria. Such methods include, for example, CRISPR/Cas
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spectrum, in topics in virology and immunology, and currently specializes in computational biology focusing on developing methods and applications of deep learning for protein sequence and structure, as
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Spatial metaTranscriptomics methods and thus also handling of image data. The PhD student will interact with other team members to a large extent. For this purpose, we are looking for a PhD student with
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data as well as other modalities (i.e. microbiome) generated using the Spatial metaTranscriptomics (SmT) method. In addition, the PhD student will also apply Spatial Transcriptomics and single-cell
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on how your research can be further developed into innovations. You are interested in driving the integration of methods in artificial intelligence (AI) and machine learning (ML) to improve and optimize
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evolutionary analysis. A central component of the research will be to develop machine learning and deep learning methods trained on coding sequences and protein structure to extract patterns in data and to draw