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influencing drug efficacy and safety. The project addresses a major bottleneck in drug discovery—metabolite identification, which is traditionally time- and resource-intensive. By leveraging deep learning
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, within the Centre for Image Analysis at the Department of IT and conducted alongside researchers developing computational methods with a particular focus on deep learning and image analysis. The project
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Experience in deep learning/generative AI or molecular modelling Prior research or industrial exposure Ability to work in a multidisciplinary and collaborative environment How to apply: The application
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variability in risk factor susceptibility, treatment response, disease pathogenesis, and clinical diagnosis (biostatistics, machine/deep learning), ii) Investigating causal processes and disease mechanisms
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the demand for energy storage soars, lithium-ion batteries lead the charge—but are they truly the most sustainable option? We are diving deep into the environmental performance of emerging battery
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information. The techniques include image registration, segmentation, and regression/classification, often include deep learning-base implementations. Together with experts in epidemiology, genetic, and multi
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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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practical applications. Experience in machine learning and AI, particularly deep learning frameworks such as TensorFlow, and their application in fluid dynamics and heat transfer research. Programming skills
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, and will apply deep learning to integrate the analysis flows. The PhD student will develop the method and apply to numerous in-house samples of environmental sequences, pushing the boundaries of RNA
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data analysis, programming, and biology. You will be part of a collaborative research team with deep experimental and analytical expertise, with access to advanced tumor models and state-of-the-art