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Computing Center (MGHPCC). You will help with optimization of code and utilization of scheduler features to maximize throughput of jobs. In addition, you will work with research groups to help identify
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and run it efficiently on different hardware architectures. For example, Google has built TensorFlow, a framework for deep learning allowing users to run deep learning on multiple hardware architectures
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. Professional Experience: Implementation of data analysis and optimization methods, predictive models and Machine Learning. In the use of external APIs from multiple sources. Experience in functions similar
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As a Procurement Lead, you will play a pivotal role in ensuring our University's success by optimizing our procurement processes, building strong supplier relationships, and driving cost savings
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research that covers the energy value chain from generation to innovative end-use solutions, motivated by industrialisation and deployment. ERI@N has multiple Interdisciplinary Research Programmes which
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-scale analyses across diverse cohorts, leveraging PacBio HiFi, Oxford Nanopore, and graph-based genome technologies. Job Duties Develop, implement, and optimize algorithms and computational methods
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for deep learning allowing users to run deep learning on multiple hardware architectures without changing the code. Our research team at NYUAD (New York University Abu Dhabi) is developing a new
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the area of optimization and control for energy-efficient systems. The position collaborates on and assists with research in traffic modeling and optimal control, optimal control of infinite-dimensional
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Max Planck Institute for Biology Tübingen, Tübingen | Bingen am Rhein, Rheinland Pfalz | Germany | 2 days ago
multiple species, with a focus on how new sex determination systems emerge and evolve. To address these questions, we combine high-throughput genomics and transcriptomics analyses with functional and
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. For example, Google has built TensorFlow, a framework for deep learning allowing users to run deep learning on multiple hardware architectures without changing the code. Our research team at NYUAD (New York