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components in time and space, from single molecules to native tissue environments. The project The industrial PhD student will develop AI and machine learning models to predict drug metabolism, a critical area
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evaluating designed backbones and predicting the functional effects of protein variants. In addition, the doctoral student will be part of the DDLS initiative, and participate in the DDLS Research School
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methods, including modern machine learning methods, to draw inferences from register data. A third project “Integrative machine and deep learning models for predictive analysis in complex disease areas“ is
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flow phenomena. The goal is to integrate theoretical and experimental fluid dynamics with modern computational tools to analyze and predict multiphase flow behavior. The project also involves applying
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immunity and develop diagnostic approaches that accurately predict therapy benefit and enable successful individualized cancer therapy planning. Contemporary AI-based approaches show great promise to advance
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theory (DFT) and related computational methods. Your work will contribute to predicting and deepening our understanding of electronic, structural, and magnetic properties at solid-state surfaces and
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systems uses data to improve their own performance, understanding, and to make accurate predictions and has a close connection to applications. Project description The research projects on sustainable
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or predictive modelling of pathogen biology or host-microbe systems for which multidimensional, genome-scale experimental data are now available or it may use population-scale genetic, clinical, or public health
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clinical prediction of progression remains difficult, leading to over- and undertreatment of women with particularly early breast cancer. Over the past decade, spatial tissue analysis techniques have been
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prediction or patient stratification based on, among other things, molecular data. More information about the group’s activities can be found at: https://www.uu.se/en/department/immunology-genetics-and