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year project, funded by the DDLS program, we aim to develop AI-based tools in design of affinity ligands, such as the prediction of binding interactions between proteins. Data-driven life science (DDLS
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for computational protein design, but also for an array of other applications related to modeling and understanding proteins and their behavior. The heart of the project will be the development and refinement
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data. For this, the applicant will generate in silico datasets from diverse computational models of development, such as models of tooth development and gene regulatory networks. They will leverage
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to the advancement of precision medicine in oncology. A typical workday may involve writing and running code to pre-process sequencing data on a compute server, applying statistical models and algorithms to construct
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Department of Ecology We are looking for a postdoc/researcher to develop and implement tools for analysis of output from Bayesian inference under phylogenetic models About the position A postdoc
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, including high-throughput screening, high-content imaging, omics technologies, and computational approaches, to elucidate mechanisms of toxicity. Ultimately, our work contributes to a deeper understanding of
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KTH Royal Institute of Technology, School of Electrical Engineering and Computer Science Project description Third-cycle subject: Computer Science This project involves generative modeling
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. For more information, see www.iob.uu.se . The Physiology and Environmental Toxicology program is a growing, vibrant research environment where experimental models and molecular tools are used to study and
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at least 1 million DNA barcodes. The project involves collaboration with a computer vision lab at Linköping University, focused on developing AI-assisted techniques for picking out specimens for genome
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, computational modelling, bioinformatic analysis, and experimental vascular biology. Based in a dynamic translational research environment of data-driven life science, computational imaging, and vascular surgery