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project at the forefront of microscopy and bioimage analysis (https://www.msca-agile.eu/ ) and contribute to OMERO integration and optimization of Biom3d, a cutting-edge deep-learning framework for 3D image
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testing (NDT), and the mechanics of advanced materials, particularly composites and fatigue-critical structures. A key research direction involves the integration of machine learning and deep learning
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While deep learning has shown remarkable performance in medical imaging benchmarks, translating these results to real-world clinical deployment remains challenging. Models trained on data from one
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-generation AI models of gene regulation and disease progression. Our work is redefining Parkinson’s disease biology and enabling translational breakthroughs. The role Develop deep learning models across genome
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, a novel spatial discovery proteomics concept that integrates microscopic cell phenotyping with deep-learning based image analysis and global MS-based proteomics. This unique method was recently
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diseases, Genome Biology, 2024 S. Hudaiberdiev et al., Modeling islet enhancers using deep learning identifies candidate causal variants at loci associated with T2D and glycemic traits, PNAS, 2023 S. Li et
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Experience with MATLAB/Simulink, including Control System Toolbox, System Identification Toolbox, or Deep Learning Toolbox. Understanding of battery systems, electrochemical energy storage, or battery
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, biomedical engineering, medical imaging, or related field Experience in deep learning with practical implementation Strong Python skills and relevant frameworks Experience with large clinical imaging datasets
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Background and Motivation Modern deep learning models have achieved remarkable success in computer vision and natural language processing. However, they typically produce overconfident predictions
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with Bayesian inference or machine learning is an advantage. • Proficiency in Python and/or R; experience with probabilistic programming (Stan, PyMC, NumPyro) or deep learning frameworks (PyTorch, JAX