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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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for structural biology. This project sits at the intersection of X-ray scattering and deep learning, aimed at integrating experimental data to predict protein ensemble structures. As an Empire AI-funded fellow
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monitoring agricultural emissions across Africa using satellite remote sensing, atmospheric modeling, and deep learning. Research Focus Estimate cropland emissions (NH3, N2O, CO2, CH4) using satellite
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learning, and AI applications in radiology. The research area includes innovative work on developing Deep Learning Based Image reconstruction in CT on Photon Counting Detector CT with work in collaboration
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during surgery or endoscopic exploration. This postdoctoral position aims at developing innovative deep learning algorithms to help histology classification. Both classical histology based on hematoxylin
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conferences (e.g., NeurIPS, ICML, ACL, EMNLP, etc.). Proficiency in programming languages such as Python, and experience with deep learning frameworks like TensorFlow, PyTorch, or JAX. In-depth understanding
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, multimodal, and agentic AI, as well as foundation models, with a focus on geometric deep learning, large-scale knowledge graphs, and large language models. Fellows will also have the opportunity to apply
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documented expertise in: Modeling and simulation of physical systems, Deep learning with applications in robotics, in particular field robotics, and Control and motion planning of mechatronic systems
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evaluate innovative methods based on generative models and Vision-Language Models. Design, implement, and validate deep learning approaches for vision applications. Publish research results in leading
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skills. Particularly meritorious is documented expertise in: Modeling and simulation of physical systems, Deep learning with applications in robotics, in particular field robotics, and Control and motion