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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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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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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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, 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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the country, substantially equivalent knowledge strong programming skills, esp. deep learning prior education and research experience in machine learning In addition to the above, there is also a mandatory
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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
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learning, deep learning and relevant software framework (R and Python) is highly desired. Very good oral and written communication skills in English are required. Emphasis will also be given on personal
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. The long-term goal is to enable targeted interventions for the right individuals, based on their lifestyle, disease trajectories, and molecular profiles. To achieve this, we will apply deep learning models