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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 3 months ago
information about staff equity at ANU, visit https://services.anu.edu.au/human-resources/respect-inclusion We welcome and develop diversity of backgrounds, experiences and ideas and encourage applications from
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develop algorithms that align image level embeddings across modalities (e.g., fluorescence ↔ electron microscopy ↔ brightfield ↔ …). In collaboration with other engineers and scientists, you will use
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Develop and implement advanced data analysis algorithms and artificial intelligence methods Analyze data from LIGO/Virgo/KAGRA detectors to search for and characterize gravitational wave signals Collaborate
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century. Join the Centre de Résonance Magnétique des Systèmes Biologiques (CRMSB ) – of the University of Bordeaux! This mixed research unit develops research themes in the field of MRI methodology and
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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description We are seeking for a highly motivated postdoctoral researcher to develop
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, Optimization, Machine Learning, Reinforcement Learning, Computer Vision, and Pattern Recognition, as well as contributing to curriculum development. About NYU Abu Dhabi https://nyuad.nyu.edu/en/ NYU Abu Dhabi is
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acceleration of large-scale machine learning workloads Perform characterization and modeling of electronic and optical devices Develop hardware-aware machine learning models incorporating electronic and optical
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payment for that month). 2.5. Tasks to be carried out: Developing deep learning methods for processing multimodal data and data for analysing temporal information. Developing computer vision algorithms with
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targets the development of advanced grey-box modeling frameworks for multiphase flow systems, combining mechanistic, multi-scale flow models with data-driven inference and uncertainty quantification
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targets the development of advanced grey-box modeling frameworks for multiphase flow systems, combining mechanistic, multi-scale flow models with data-driven inference and uncertainty quantification