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advanced AI/ML methods for robust analysis and integration. Data sparsity, batch effects, and missing values across different omics layers and platforms. Cross-omics data fusion and representation learning
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-aware multi-modal deep learning (DL) methods. At Argonne, we are developing physics-aware DL models for scientific data analysis, autonomous experiments and instrument tuning. By incorporating prior
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informed neural networks (PINN) and explainable machine learning (EML) frameworks; experience in related technologies including large-scale data analysis, deep learning, Python, PyTorch; and the ability
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implementation of deep learning and computer vision frameworks across a range of research projects. This includes developing and training deep learning models for tasks such as scene understanding, object
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leverage state-of-the-art deep learning techniques to address challenges in visual data processing and forensic analysis. As part of a dedicated, collaborative research team, you will push the boundaries
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at least one year of postdoctoral research, have experience with data visualisation, and be enthusiastic about engaging with different online communities to learn more about public uses of the past. They
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learning. The employment is full-time for two years starting from August 1st 2025 or by agreement. Apply latest April 7th 2025. Project description Geometric deep learning refers to the study of machine
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/expression conservation and the mechanistic modeling. You will develop machine learning/deep learning-based bioinformatics methods to translate gene regulation modules between species by integrating orthology
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Biljecki (National University of Singapore). Your key responsibilities will be: 1. Co-developing the research of work package 1. This work includes, a.o.: Developing deep learning models for the project’s
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communication and collaborative skills. Experience with SLAM, sensor fusion, LiDAR/depth camera data processing. Familiarity with deep learning for obstacle avoidance (e.g., map-less navigation). Background in