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clinical service, appointments of trust in trade union organizations, or similar circumstances. Doctoral degree should be within bioinformatics, machine-learning, computational biology, genomics, or a
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The Rantalainen group is focused on application of machine learning and AI for development and validation of predictive models for cancer precision medicine, with a particular focus computational pathology. Our
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environment project, we will develop automated species and community recognition, particularly focusing on pathogenic soil fungi, with help of deep-learning algorithms fed with microscopic image and Raman
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bioinformatics, with a particular emphasis on performing analysis of high-dimensional data, which can be sequencing and/or imaging-based. Experience working with AI and machine learning approaches are considered a
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and Data Science for Spatial Genomics in Diabetes This position centers on the development and application of machine learning, image analysis, and integrative omics approaches to spatial
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systems, and machine learning. While the initial focus of the position is on this project, we offer significant opportunity for the applicant to develop their own independent research trajectory in
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sequencing, and with computer scientists at KTH in Stockholm, focused on developing scalable probabilistic machine learning techniques for online phylogenomic analysis and placement of DNA barcodes. You will
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The position's field of research focuses on developing and implementing safe, transparent, and explainable AI systems using multimodal deep learning and Large Language Models (LLMs) for healthcare
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passive and active flow control algorithms, potentially incorporating machine learning/AI, to enhance aerodynamic performance and stall delay with rapid response times. The research is conducted in
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application! Work assignments Subject area: Computational studies of the influence of microstructural features on the structural integrity of metallic materials using machine learning Subject area description