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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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spectral sensor data. By using absorbance data, you will train models to predict key water quality indicators such as nitrate, nitrite, and dissolved organic carbon in both freshwater and marine environments
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from automated vehicles (AVs), they must be both safe and appreciated by drivers. This project uses modeling (e.g., AI/machine learning) and human behavior data to predict perceived safety and quantify
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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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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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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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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
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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