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Field
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architectures for foundation models The work will combine methodological development with large-scale experiments, aiming for contributions at leading machine learning and computer vision venues such as NeurIPS
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machine learning-based model to map satellite retrievals to ground based air pollutant concentrations Conducting error assessment on the derived concentration data Implementing new observational data
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The successful candidate will develop generative machine-learning methods for amorphous molecular thin films — the supramolecular structures that govern the performance of organic-electronic materials
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additional days off (e.g. between Christmas and New Year's) Flexibility: Flexible working time models, including options close to full-time , allow you to tailor your working hours to suit your individual
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | about 1 month ago
highly motivated AI Scientist (f/m/x) (best: Computational Pathologist / Machine Learning Scientist for Digital Pathology) to join our efforts in developing AI-driven virtual staining pipelines for cancer
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uncertainty, since misclassifications can lead to poor energy management decisions or inappropriate building automation responses. The goal of this project is to design and validate a machine learning framework
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methods You will work closely with: - Dr. Martin Ramacher (machine learning for environmental applications) - Dr. Matthias Karl (urban air quality modelling and emissions) and collaborate within a project
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modeling, and scientific machine learning to enable efficient simulations of complex molecular processes across realistic time and length scales. https://doi.org/10.48550/arXiv.2604.24245 https://doi.org
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establish a research profile. Develop and execute innovative research projects. Develop, train, and evaluate modern machine-learning models on GPU/HPC infrastructure. Integrate AI methods with scientific
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machine learning Reinforcement learning, planning and sequential decision-making Scaling, compression, and efficient training of multimodal and foundation models Self-supervised, contrastive, continual