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Field
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opportunity to collaborate together and learn methods from the parallel project, building a broad skillset. Both positions are part of the larger project “Bringing the wild into the lab with Virtual Reality
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LevelPhD or equivalent Skills/Qualifications Who you are You have a PhD, preferable in Computer Science, Engineering, Mathematics, or equivalent. You have a strong background with parallel and distributed
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environments, parallel and distributed file systems, tiered storage strategies, and hybrid cloud integration. Strong communication skills across technical and non-technical audiences Strong understanding
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, nonlinear dynamical systems, robotics, and formal methods to develop principled models and algorithms for distributed decision-making in complex and uncertain environments. Your research The candidate will
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computational approaches, including in vivo Massively Parallel Reporter Assays (MPRAs), to define the sequence basis and functional consequences of enhancer activity and to expand MPRA-based approaches to other
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of cluster and job/resource management software (e.g., Warewulf, Slurm, XCat) Parallel and distributed file system (Lustre) experience is a plus Experience with the installation, configuration and
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Qualifications Experience: Relevant programming experience developing, implementing, debugging, and maintaining applications with Python. Experience working with high performance computers (e.g., parallelizing and
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functional genomics, human genetics, and in vivo experimental systems to understand enhancer function across regulatory and phenotypic scales. We develop and apply both experimental and computational
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Journal (special issue on Kolmogorov complexity), Vol. 42, No. 4, pp270-283 Wallace, C.S. and D.L. Dowe (2000). MML clustering of multi-state, Poisson, von Mises circular and Gaussian distributions
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The project: The domain of High Performance Computing (HPC) effectively utilises massively multicore computers that facilitate the distribution of scientific workloads across thousands of compute