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Background in machine learning or deep learning methods, including Graph Neural Network (GNN) Experience with Large Language Models (LLMs) applied to biological data collection, extraction, and standardization
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of Virginia (UVA) School of Medicine is seeking to fill Postdoctoral Researcher positions in computational biology and machine learning. The lab develops modern machine learning, generative modeling, and
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methods with the ability to implement and evaluate machine-learning systems at scale. Candidates may come from topological data analysis, geometric deep learning, network science, statistical physics
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development opportunities In addition to exciting tasks and a collegial working environment, we offer you much more: https://go.fzj.de/benefits To apply, please submit a complete CV including a publication list
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, with a particular focus on the iron and steel sector. By using TROPOMI observations with advanced machine learning techniques, the project will provide independent information on emission patterns and
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of thousands of molecules in our fully automated high biosafety screening facility CAPS-IT against multiple viruses. You will be responsible for the development and deployment of advanced machine learning models
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the department and the funding agencies. The successful candidate will join the Machine Learning & Data Science research group and conduct research on the mathematical theory of deep learning as part of the joint
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of engagement. Read more at: Department of Computing Science Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data-driven
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the intersection of machine learning (ML) and the sounds of wildlife (“bioacoustics”). We are also happy to consider candidates in one of the two fields who can demonstrate a strong basis for working in this cross
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We are seeking a postdoctoral researcher with a curiosity-driven record who works at the intersection of machine learning (ML) and the sounds of wildlife (“bioacoustics”). We are also happy