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Field
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journal publications dependent on your background discipline(s) and should hold sufficient theoretical knowledge of deep learning-based methodologies as well as working with real-world data. Informal
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simulations of PDEs, deep learning, neural networks. Our research interest: Our focus is on theoretical and computational biological physics, ranging from the study of molecules to cells. We strive to leverage
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postdoctoral researcher for pioneering research at the convergence of artificial intelligence (AI) and materials science. The ideal candidate should possess expertise in scientific machine learning (SciML
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learning methods. Develop deep learning architectures (e.g., variational autoencoders, graph neural networks, transformers) for cross-omics data representation and feature extraction. Apply multi-view
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learning, deep learning, and large language models (LLMs), for the analysis of high-throughput multi-omics datasets (especially single-cell and spatial omics) and large textual corpora (e.g., scientific
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Control engineering (experience with nonlinear systems is a plus) Machine learning and deep learning in context of physical systems Programming skills are required, with Python experience preferred. A good
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are comfortable navigating complex HPC environments and wrangling large datasets. You have experience with modelling through state-of-the-art machine and deep-learning methods and with hands
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-traditional, e.g., event data) and network structures (for sensor networks). In this project, we will investigate Bayesian deep learning approaches to training models under uncertainty for several sensing
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CBS - Postdoctoral Position, Artificial Intelligence Applied to Metabolomics for Health Applications
metabolomics data from clinical studies. Apply deep learning models (e.g., autoencoders, variational autoencoders, graph neural networks) for biomarker discovery, disease classification, and patient
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. Preferred: • Strong programming skills in languages such as R/Python • Research background in biostatistics/statistical genetics/population genetics/deep learning and LLM • Experience in any of the following