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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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-made nanoaggregates as drug nanocarriers. Project description: Drug nanocarriers today rely primarily on lipid-based nanoparticles. However, these particles have limitations, for example they suffer from
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staff based in Lund, Helsingborg and Malmö. We are united in our efforts to understand, explain and improve our world and the human condition. Lund University welcomes applicants with diverse backgrounds
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. The student will work in a group addressing all these challenges, developing new AI-based methods to improve biological realism in simulations which will lead to more accurately inferred GRNs from real data
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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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of molecular pharmacology, biochemical, and cellular assays to discover new small molecule and antibody-based prototype drugs. SciLifeLab DDD maintains a semi-industrial environment within the universities where
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position based at SciLifeLab in Stockholm. The project focus on characterizing phenotypic and genomic variation associated with seasonal camouflage variation in willow grouse (Lagopus lagopus). The analysis
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information. The techniques include image registration, segmentation, and regression/classification, often include deep learning-base implementations. Together with experts in epidemiology, genetic, and multi
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courses equivalent to at least 60 credits in a mathematical subject and at least 30 credits in either numerical analysis or computer Selection The selection among the eligible candidates will be based
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! The research group The PhD student will be based both at AstraZeneca, Gothenburg, and at Chalmers University of Technology. At Chalmers, the student will be a part of the Holme Lab, based