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computing (HPC) and parallel processing to enable the analysis of massive datasets. Experience in advanced statistical inference (e.g., Bayesian statistics, spectral methods) for extracting robust patterns
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 22 hours ago
projects using massively parallel reporter assays (MPRA) to quantify the impacts of disease-related genetic variants. Finally, they will interact with nearby collaborators to plan and analyze functional
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
: Expertise in rare event simulation, deep learning, and developing computationally efficient approaches for simulation and modeling in complex systems is highly desirable Experience with parallel computing
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redox balance. Our studies explore how p53 integrates metabolic cues by acting as both a sensor and regulator of cellular metabolism. In parallel, we are identifying metabolic changes that promote tumor
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develop computational fluid dynamic (CFD) tools that make exascale computing accessible to a broader set of users. The successful candidate will develop a massively parallel solver, capable of running
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associated with biological membranes, including large peripheral membrane complexes and integral membrane transporters. In parallel with mechanistic structural work, we develop targeted chemical probes
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(HPC): Experience with parallel computing (MPI, OpenMP, CUDA/HIP) or running workflows on supercomputing clusters. Software Engineering: Knowledge of version control (Git), containerization (Docker
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will have the opportunity to work with: High‑throughput functional genomics: pooled CRISPR and base‑editing screens, barcoded overexpression libraries, massively parallel reporter assays. Advanced cell
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collaborating experimental research groups. Previous experience in computational modeling of atmospheric aerosols and parallel computing/software development is strongly desired. The term of appointment is based
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). Expertise in data and model parallelisms for distributed training on large GPU-based machines is essential. Candidates with experience using diffusion-based or other generative AI methods as