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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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. Preferred Qualifications Experience with: C/C++, Python, MATLAB, ROS 1 and 2, OpenCV, Unity, GPU programming, linear and nonlinear control theory, supervised, unsupervised and reinforcement learning, Torch
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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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implemented in the Fortran programming language, and it relies on the platform CUDA for parallelization of the computation over several GPUs’ cores, and has interfaces with Matlab and Python for ease of use
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performance computing numerical methods in our state-of-the-art open source micromagnetic model, MagTense. MagTense is based on a core implemented in the Fortran programming language, and it relies
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++ and Python programming languages. Experience in open source projects, GPU programming, distributed computing and cloud computing are considered to be strong assets. The position of Research Fellow at
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, cancer genomics, or a related field. Applicants with a background in biology or (bio)medicine are welcome to apply, provided they have documented expertise in deep learning Proficiency in programming
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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
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electricity prices with focus on Nordic electricity market including implementation, test and validation at the DTU Risø HPP facility (possibly in a GPU computing infrastructure) Aid the implementation of IEA
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be direct access to advanced biophysical infrastructure in the biophysics core facility headed by the PI, a GPU cluster with working pipelines for computational design and the department’s bioimaging