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Field
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. Knowledge and Professional Experience: DFT-based methods. Scientific programming in Fortran, in MPI/OpenMP-parallelised codes. Knowledge of other languages (in particular python) and of GPU offloading will be
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developing machine learning surrogates and emulators for dynamical systems. Proficiency in managing large datasets and training with GPU-enabled computing resources. Expertise in numerical optimization and
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(GPUs). Motivated self-starter with the ability to work independently and to participate creatively in collaborative teams across the laboratory. Ability to function well in a fast-paced research
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(Jubail) dedicated to the science division, several GPU-based clusters at NYUAD, and other supercomputer facilities through the CASS network. NYUAD also has guaranteed observing time on the Green Bank
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with structure and function of microbial communities, primarily related to environmental biotechnology. The Center has state-of-the-art equipment within DNA sequencing, laboratory automation, CPU and GPU
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contact, as identified by AFRL through recent past efforts. This includes the implementation of relevant algorithms and solvers for distributed GPU computing within the JAX Python library. Qualifications
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contact, as identified by AFRL through recent past efforts. This includes the implementation of relevant algorithms and solvers for distributed GPU computing within the JAX Python library. Qualifications
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managing experiments using GPUs Ability to visualize experimental results and learning curves Effective inter-personal and team-building skills Self-motivated with an ability to work independently and in a
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and optimize large-scale training and inference runs for foundation models on JUPITER (multi-GPU/node, mixed precision, parallelization, I/O optimization) Integrate multimodal data sources (e.g., scRNA
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within DNA sequencing, laboratory automation, CPU and GPU compute resources, proteomics, metabolomics and advanced microscopy. Your competencies Documented experience with large scale microbial