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Serve as the Lead for the team ensuring smooth operation of the Linux cluster consisting of 300+ GPU/CPU compute nodes including parallel filesystems and high-performance network. This is partly
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computing frameworks (e.g., MPI, NCCL) and model parallelism techniques. Proficiency in C++/CUDA programming for GPU acceleration. Experience in optimizing deep learning models for inference (e.g., using
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protein expression and purification, capable of producing thousands of proteins in parallel within weeks . 2) Eukaryotic expression systems facility for production of challenging protein targets. 3) A fully
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large-scale simultaneous recordings of hippocampal neurons during virtual reality, with focus on hippocampal area CA1. Additionally, work to develop theoretical network models that parallel
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on several aspects to explore in parallel: - Develop the experimental set-up to enable Low-Energy SAXS measurements on Attolab’s set-up (through punctual mission (2 or 3 times a year for 1 to 2 weeks
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". A number of emerging approaches, such as zero resource and unsupervised NMT, have investigated alternative methods in developing NMT models where sufficient parallel corpora are not available (eg [1,2
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on methods development in machine learning, uncertainty quantification and high performance computing with context of applications from the natural sciences, engineering and beyond. It is embedded in
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method, can engineer thousands of defined mutations in parallel in a single test tube in yeast. Strains are tagged by DNA barcodes, allowing to efficiently track mutations in cell populations during
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computing clusters, parallel processing, and data pipelining. Strong analytical and statistical background, with the capability to interpret complex biomedical data accurately. Direct experience handling and
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clinical settings and working to identify and deliver opportunities for engagement and collaboration within the university and beyond. The ability to contribute to research impact, program innovation