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Fellow in Deep learning for medical image analysis Apply for this job See advertisement About the position Position as PhD Research Fellow in Deep learning for medical image analysis available
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-Based Maintenance Planning for Wind Turbine Components. This position is part of the MET2ADAPT Doctoral Network (Meta-Materials and Meta-Structures for Adaptable, Resilient and Sustainable Renewable
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. The program emphasizes faculty-mentored research, teaching innovation, and public engagement, providing Fellows with resources and networks to launch impactful careers. To learn more about the SCALES
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Master’s degree and seven years of relevant experience. Relevant experience in higher education and data analysis. Knowledge of WashU systems, including SLATE and Workday. Ability to relate well to a variety
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datasets Implement LLM and Machine Learning algorithm Conduct statistical analysis in SAS or Stata Assistant with other ad hoc tasks Required Education Bachelor’s or Master’s degree in computer science
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funded PhD position on Uncertainty Quantification and Technology Qualification for Advanced Wind Turbine Components. This position is part of the MET2ADAPT Doctoral Network (Meta-Materials and Meta
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with the faculty of Bioengineering and Biology at NYUAD. Responsibilities of the position include growing cells in organ-on-a-chip microfluidic devices, characterizing them using downstream analysis
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troubleshooting wireless networks and variety of wireless clients, fiber optic and copper plant topologies, Voice over IP technologies, knowledge of network analysis, development, programming, and understanding
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molecular simulations, and cutting-edge AI techniques including graph neural networks (GNNs) and large language models (LLMs) to accelerate experimental design and discovery of novel materials. The research
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EUMETSAT - European Organisation for the Exploitation of Meteorological Satellites | Darmstadt, Hessen | Germany | 10 days ago
products and cloud properties. Other space missions also operated by EUMETSAT, such as the Copernicus missions, will contribute to this network of cloud observations. Within the Clouds and Aerosols