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FWO-UGent funded bioinformatics postdocs: Unveiling the significance of gene loss in plant evolution
, or related fields Strong programming skills in Python, R, and experience with Linux environments Demonstrated experience in processing and analyzing large-scale genomic and transcriptomic datasets
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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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proteins to specific locations on the genome. The project involves grid preparation and screening, data collection and image processing using the Emory cryoEM core and our lab’s cluster (200 TB and 8 GPU
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for earth system science C++ programming skills and model simulations on GPUs E3SM, CESM, and WRF model experience Job Family Postdoctoral Job Profile Postdoctoral Appointee Worker Type Long-Term (Fixed Term
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experimental data. Experience in GPU programming. Job Family Postdoctoral Job Profile Postdoctoral Appointee Worker Type Long-Term (Fixed Term) Time Type Full time The expected hiring range for this position is
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experience with coding in C++, python/matlab. GPU programming is a definite plus. You have experience in applying deep learning to solve computational imaging problems. Experience with inverse problems such as
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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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conferences and/or leading scientific journals. Excellent programming skills and hands-on experience with leading machine learning frameworks (e.g., TensorFlow, PyTorch). Practical experience with cloud
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in Applied Mathematics, Computational Science and Engineering (CSE), or related fields with outstanding academic performance Strong programming skills and experience in languages such as C/C++, FORTRAN
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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