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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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. Are you interested in applying your machine learning and deep-learning expertise to develop cutting-edge ecological and environmental research? The Senckenberg Gesellschaft für Naturforschung invites you to
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foundational and applied topics in computer vision and machine learning, with particular strengths in inverse problems, generative models, and geometric deep learning. We work across diverse application areas
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successful candidate will possess skills in natural language processing and deep learning. Experience of studying the robustness and generalisability of LLM would be beneficial. This is a full time post (35
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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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interpretable deep neural networks is required. Candidate must have published in top journal and conference at least one scientific paper in interpretable machine learning (not explanations of black boxes) among
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associate will also provide leadership in coordinating different projects and advising more junior lab members. The current and prior work of the lab include deep learning algorithms for detection
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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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Stanford University required minimum for all postdoctoral scholars appointed through the Office of Postdoctoral Affairs. The FY25 minimum is $76,383. Deep Phenotyping of Learning Differences The high-level
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sustainability performance for products and services at different regional levels. We are looking for a researcher with solid experience in sustainability assessment, with life-cycle assessment (LCA) as the