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Join ABC Labs and KTH as an Industrial PhD Student to build data-driven models of human biology with multimodal health data — interdisciplinary impact, real-world healthcare. About the role We
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for understanding their reliability and for making informed decisions based on their predictions. This project aims to develop new methods for uncertainty quantification in mathematical and statistical models
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application! We are looking for a PhD student in Medical Science, AI and Bioinformatics. Your work assignments This project aims to develop AI foundation models for integrative single-cell and multi-omics
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patients develop a durable response. Many researchers are investing efforts to understand the complexity of anti-cancer immunity and develop diagnostic approaches that accurately predict therapy benefit and
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prediction, most current models still describe proteins largely as static structures and do not fully capture the conformational ensembles that underlie protein function. This PhD project aims to address
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. At the Division of Systems and Control , we develop both theory and concrete tools to design systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and
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on community ecology. Researchers have access to excellent glasshouse and climate-controlled facilities, fully state-of-the-art molecular labs and a high-performance computing cluster (UPPMAX). The Department
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multi-omics integration with advanced machine learning, including artificial neural networks, to predict disease-relevant splice variants across cardiometabolic diseases. By leveraging extensive meta
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screening approaches in which millions of distinct nanostructures are produced as a library, incubated with cancer and control cell lines. Cellular outcome will then be used as a selection marker to identify
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Development Design new statistical and machine learning models tailored to this emerging omics modality. Multimodal Data Analysis Work with high-dimensional datasets combining quantitative RNA features