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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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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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. Project overview The project involves applying advanced statistical analysis, machine learning techniques, and modeling approaches such as agent-based modeling to analyze diverse climate and socioeconomic
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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
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to the application deadline. PhD in computer science, electrical engineering, biomedical engineering, or a related field. Experience in Python programming, natural language processing, and multimodal deep learning
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. The successful candidate will work on cutting-edge projects involving artificial intelligence (AI) and computational pathology, with a particular focus on developing and applying machine learning algorithms
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these datasets to detect chromosomal abnormalities and study their breakpoints. Using statistical methods and machine learning, we will explore how these structural variants arise and which recurring structures
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such as epidemiology, biostatistics, computer science, statistics, etc. We will also consider those with PhDs in other areas but who have advanced/relevant data science skills (e.g., machine learning
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psychiatry. The projects will involve advanced epidemiology, pharmacoepidemiology, and machine learning methods. You will be part of a well-funded and successful research group, collaborating with
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are looking for candidates with a PhD in Computer Science, Visualization and Media Technology, Machine Learning or a closely related research field. A strong background in machine learning and visual data