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interactions, or aerosol–cloud interactions Strong experience in numerical modeling and high-performance computing • Experience applying AI/ML methods to model development, with strong programming skills (e.g
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rigorous derivations, proofs, and analytical reasoning • Experience in mathematical modeling and the development or analysis of numerical methods for PDEs • Strong scientific programming skills in C/C++ and
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in collaboration with Texas A&M College of Engineering, Texas A&M School of Medicine and the state’s top-ranked Houston Methodist Hospital offer an innovative medical school program called ENMED
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of environmental factors of 60,000 subjects across multiple time points. Our research laboratory has great computing capacity, including multiple H100 and A100 GPU systems for deep learning, and computing clusters
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scientific domains. Preferred Experience: Strong candidates may also have experience with: Large-scale neuroimaging datasets. GPU-based model training and distributed computing. Brain connectivity modeling
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Preferred Qualifications • Experience with GPU programming, shaders, or advanced rendering techniques • Experience integrating external APIs or live data streams • Background in distributed systems or edge
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Qualifications: Ph.D. (within 0–5 years) in computational bioscience, computational biophysics, computer science, or a related field Strong programming skills in C++, Python, or similar scientific computing
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preprocessing large-scale single-cell and multi-omic datasets, defining model architectures, optimizing training pipelines on GPU clusters, and benchmarking against existing methods. Integrate and analyze large
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their publications Experience programming GPUs with CUDA, SYCL, HIP or OpenMP Experience using and developing code with AMReX Experience in performance engineering to improve code scalability and reduce time-to
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Python and experience with GPU cluster environments (e.g., SLURM) are a plus. Special Instructions Please provide a CV, a Research Statement, and two or more letters of recommendation. The target start