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mathematicians, and domain scientists Develop software that integrates machine learning and numerical techniques targeting heterogeneous architectures (GPUs and accelerators), including DOE leadership-class
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. The successful applicant will be expected to teach both undergraduate and graduate courses in Psychology and Neuroscience, and to establish an active, internationally-recognized research program. To
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when there are legal requirements, such as a license, certification, and/or registration. Additional Requirements: 1. Programming & data: Python (numpy/pandas), basic R (Seurat/tidyverse), bash; Git
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digital twin prototypes. Maintain reproducible code, experiments, and model/ dataset versioning; develop deployment artifacts (Docker, CI/CD scripts) and support cloud/HPC model training. Plan, run and
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Postdoctoral position in the development of an AI-based phenotyping system for high-throughput sc...
pests, or high-throughput phenotyping Solid background in mathematics and scientific programming (R, Python, etc.) along with effective logical reasoning skills Experience with high-performance computing
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pests, or high-throughput phenotyping Solid background in mathematics and scientific programming (R, Python, etc.) along with effective logical reasoning skills Experience with high-performance computing
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information about the lab check out: https://www.moorelabstanford.com/ . About the role: The role will be in-person with hybrid flexibility and is a perfect opportunity for someone looking for a 1-year, fixed
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data from the European XFEL facility at DESY. Project website: https://www.mpinat.mpg.de/628848/SM-Ultrafast-XRay-Diffraction Your profile Eligible candidates have strong skills in computational
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fields, applying advanced techniques such as large-scale data processing and GPU-accelerated computing. Access to state-of-the-art research facilities and a new GPU cluster. Collaborative and inclusive
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part of the core PLI team, which includes top-tier faculty, research fellows, scientists, software engineers, postdocs, and graduate students. Fellows will have access to the AI Lab GPU cluster (300