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in large pre-trained models (vision-language models), generative models (flow matching, diffusion), simulation-based inference, and robust and active learning. The group has a wide network of
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. Simulation-based inference (SBI) addresses this by training neural networks, such as flow-matching generative models, on simulated events. The project aims to develop efficient, robust and calibrated SBI
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and variation. The successful candidate will develop innovative methods and models to advance our understanding of genome evolution and variation. The position is based in the Computational Genomics
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samples, lack of training data and sample variability. In this project we aim to develop AI/ML workflows for improved quantitative analysis of LNPs. Your responsibilities will include optimisation of data
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postdoctoral in Biological Data Systems to develop scalable data infrastructure and computational systems for large biological datasets. The position will be embedded within the Human Protein Atlas at KTH and
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and directing students achieve individual and collective research goals. This provides opportunities for training in scientific leadership for eventual application as an independent group leader
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are also included as important aspects of the employment. You will also have the possibility to contribute to applications for external research funds. In this project, your responsibilities will include
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no later than the time the employment decision is made. Here you fill in additional requirements for the position, in case of any language requirements, justification is required. Contact HR if you have any
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Do you want to contribute to improving human health? Description of the research group Alpha Cell is a new SciLifeLab strategic program aimed at building the first molecular-level computational
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model organisms, their application in conifers remains limited due to challenges associated with tissue structure, nuclei isolation and sensitivity to inhibitory compounds. This project aims to develop