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Field
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work now focuses on the evolution of egg-laying behaviour and the transitions to viviparity. In parallel, we are also interested in characterizing the natural history, ecology, and phylogenetics
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massively parallel computing resources such as NERSC and Raj cluster at Marquette. Conduct research with a significant degree of independence, working with and supervising graduate students. Provide research
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interrogate appropriate experimental systems, while monitoring insights arising from parallel genetic, biochemical and molecular work in disease models at A*STAR. Qualifications A PhD in cell biology
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and tuning. Moderate research project experience training large-scale foundation models, especially pipeline/model parallelism. Track record of creating HPC software for numerical methods. Domain
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scales through component-based parallelism. The focus of the post-doc position is to design and implement this pipeline -- developing native reasoning for rich combinatorial constraints, creating tools
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Max Planck Institute for Astrophysics, Garching | Garching an der Alz, Bayern | Germany | 22 days ago
powerful MPA-owned parallel computing clusters. It has also privileged access to supercomputers at the Max Planck Computing and Data Facility. Interested scientists are invited to apply electronically
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algorithm development and application. Familiarity with common scientific programming languages such as C++. Experience in parallel programming with one or more common parallel programming models, like MPI
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project experience training large-scale foundation models, especially pipeline/model parallelism. Track record of creating HPC software for numerical methods. Domain expertise in areas like computational
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highly automated workflow. This means rather than having a researcher building one strain at a time manually, the same researcher could build hundreds or thousands of strains in parallel using our robotic
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of infectious diseases. • Experience with data analysis using statistical inference techniques. • Experience with health economic evaluations. • Experience with parallel and/or high-performance computing