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predictive machine-learning models from heterogeneous data. DSIP is actively collaborating with industrial partners and research organizations. DSIP is involved in developing Deep Learning solutions for time
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fields; proficiency in evaluating the effectiveness of learning materials; strong track record of working both independently and collaboratively; be organized, able to lead multiple projects simultaneously
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large-scale training of scientific foundation models You will collaborate with researchers across machine learning, scientific computing, materials science, and data engineering, and work with leading
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Description The Robot Learning & Control Lab (REAL Lab) at NYU Abu Dhabi is seeking an outstanding Post-Doctoral Associate to contribute to cutting-edge research in robot intelligence, machine
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interdisciplinary team spanning statistics, machine learning, genetics, and population health. You will work closely with collaborators at the Nuffield Department of Population Health (NDPH), the Big Data Institute
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collaborators and members of the ICARUS project are Professor Lars Nybo (University of Copenhagen), Professor Andreas Flouris (University of Thessaly), and Associate Professor Catharina Lerche (Bispebjerg
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), you will contribute to advanced research in the field of connected and autonomous driving. You will be involved in European and national research projects carried out in close collaboration with
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The FEderated droNe Countering system (FENCE) project is a collaboration between the University of Luxembourg - SnT and Luxembourg Tech School (LTS). It aims to propose a unique swarm-based counter
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We invite applications for a postdoctoral researcher to join the UMLFF project at the University of Luxembourg. The project aims to develop the next generation of uncertainty-aware machine-learning
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. The successful candidate will have the responsibility of developing, in collaboration with Dr Whelan and the PhD students, machine learning tools for the handling of the Mauve and MUSE datasets. They will also be