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performance, yet their atomic-scale origin and role in reactivity remain poorly understood. The project addresses this open problem by integrating high-throughput Density Functional Theory, machine-learning
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machine learning models. Working with extremely large, multi-modal datasets. Prior experience in analysis of clinical health records, and time series data are highly preferred. Qualifications Requirements
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with leading machine learning frameworks and modern AI environments, including multi-GPU model training and large-scale inference on dozens to hundreds GPUs, are required. Additional Qualifications
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experience Practical experience in machine learning and the application of large language models Knowledge of OMICS and image data analysis A willingness to engage in interdisciplinary scientific work
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. This PhD project aims to create advanced XCT workflows by developing Artificial Intelligence (AI) and Machine Learning (ML) tools to support imaging before the reconstruction phase. The research will focus
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identification, optimization, or numerical methods is valuable, as is knowledge of data analysis and machine learning for complex, high-dimensional systems. Programming experience in MATLAB or Python, and an
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for an academic or research-oriented career in machine learning or statistics The purpose of the fellowship is research training leading to the successful completion of a PhD degree. For more information see
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to demonstrate documented proficiency in English. You have knowledge and expertise in computer vision and/or medical image analysis, deep learning as well as mathematics. You have substantial expertise in
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Rising Innovative city . The position is formally based at the Division of Statistics and Machine Learning (STIMA) within the Department of Computer and Information Science. At STIMA we conduct research
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. or Diploma in bioinformatics or a comparable qualification Extensive programming experience Practical experience in machine learning and the application of large language models Knowledge of OMICS and image