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Field
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group and develop an innovative and impactful research program, which includes, but is not limited to, population and evolutionary genetics, metagenomics, eDNA-based monitoring, and phylogenomics
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turbulence. Experience with GPU programming, FPGA, and DNN in image recognition is a great plus. Track record of publications and conference presentations. Experience with hands on lab work. FLSA Exempt Full
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. First, efficient and scalable training procedure are still needed, irrespective of whether the training is done off-line on a traditional GPU-based architecture, on neuromorphic hardware. Second
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. The successful candidate will lead an innovative research program in cutting-edge AI computer system technologies. The position requires knowledge of both hardware and software aspects of large-scale AI
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for individuals with a passion for AI research, strong programming skills, and a desire to work at the intersection of engineering and scientific discovery. The role will also provide teaching and training
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for scientific discovery, addressing critical societal challenges in biotechnology, clean energy, and emerging technologies. Responsibilities include: Leading a well-funded research program in AI-driven scientific
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for the research team. Contribute to software solutions by adhering to project-specific best practices, including version control, continuous integration and delivery, software design, and programming models. Ensure
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computational infrastructure such as A100 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
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information from ‘deep tissue phenotyping’ datasets. The successful applicant will have significant experience working with machine learning algorithms. They will have strong Python programming skills and
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programming skills (Matlab/Python, C(++)) and experience with deep learning frameworks such as PyTorch, TensorFlow, Keras has been in your focus. Further, experience with standard supervised machine learning