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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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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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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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, 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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, 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
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millions of species. However, computational challenges exist in using these massive genomics datasets for species delimitation. The multispecies coalescent (MSC) model offers a framework for understanding