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ELTI Rwanda Post Doctoral Associate About ELTI The Environmental Leadership & Training Initiative (ELTI) was established in 2006 at the Yale School of the Environment (YSE) as a Center and Program
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and H100 GPUs, combined with pre-processed large-scale biobank data such as UK Biobank and ADSP, enabling you to work at the scale required for breakthrough research. The role offers exceptional
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completed Ph.D. within the last 5 years in Computational Biology, Bioinformatics, Machine Learning, Artificial Intelligence, Virology, or a related field Strong programming skills in Python, R, or Julia, with
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mathematics and engineering. The Interpretable Machine Learning Lab has dedicated access to high-performance CPU and GPU computing resources provided by Duke University’s Research Computing unit and state
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will create a personalized training and development plan with the supervisor. Minimum Qualifications Currently has or is in the process of completing a PhD, MD/PhD, DPhil or equivalent terminal degree
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Dr Edward Johns, as well as a larger team across the UK as part of the ARIA-funded Robot Dexterity programme (see: https://www.aria.org.uk/opportunity-spaces/smarter-robot-bodies/robot-dexterity
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techniques. Preferred Qualifications: Knowledge of HPC matrix, tensor and graph algorithms. Knowledge of GPU CUDA and HIP programming Knowledge on distributed algorithms using MPI and other frameworks such as
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Mathematica and Python with an interest in GPU programming. These required and desired skills should be demonstrated by presenting an existing body of code and/or peer-reviewed publications. Additional
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for modern heterogeneous architectures, including CPUs, GPUs, and other accelerators, the project seeks to achieve unprecedented efficiency and resolution in plasma simulations. This advancement will enable
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libraries for modern architectures (e.g., GPUs). Exploration of linear algebra methods in computational physics applications and machine learning. Integrate and benchmark the GINGKO library, a sparse solver