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learning methods to understand and investigate the functional behavior of gender-specific cancers. The work will include: Development of ML/DL methods for multi-omics data analysis. Design and implementation
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School of Engineering Sciences in Chemistry, Biotechnology and Health at KTH Project description Third-cycle subject: Biotechnology The project aims to develop methods in three-dimensional spatial
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of complex brain processes. The prospective PhD candidate collects brain MSI data and develops novel machine learning methods in connection to generative models such as flow matching. Therefore, the doctoral
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workflows for large epidemiological cohorts and clinical projects, supporting both tailored user analyses and internal projects. A key component will be the development of pipelines and applications for data
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epidemiology and biology of infection, which is a fully funded, four-year PhD student position. Data-driven life science Research School Data-driven life science (DDLS) uses data, computational methods, and
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. The project focuses on developing novel representation learning and generative modeling methods to construct a unified cellular morphology state space across heterogeneous datasets. By leveraging shared
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methods for analysis of cellular and molecular biology data. The host institution, The Department of Gene Technology , is the most prominent research environment at KTH, according to bibliometrics
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applications. Documenting experiments and method development for project tracking and publications; being updated with current with relevant technologies and scientific literature; and participating in
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contribute new and better ways to analyse and interpret large-scale data. In your position, you will develop computational methods for cryo-EM reconstruction, heterogeneity analysis, and modeling of structural
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of very different backgrounds Ability to collaborate and work in a service-oriented organization. Willingness to learn new methods and the ability to develop new skills. Ability to manage projects