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Field
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teams to codesign hardware, algorithms, benchmarks and software for QHPC systems, aiming to advance our strategic goals in leveraging quantum computing and high-performance computing (HPC) to develop
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 19 hours ago
, the postdoctoral trainee will attend weekly multidisciplinary tumor boards and shadow a provider in clinic one day per week, attend weekly breast program laboratory meeting and weekly meetings with supervisor and
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: Bachelor’s degree in a biological science, health science, or related field, or equivalent combination of education and relevant experience. Proficiency in digital and computational pathology, AI/ML algorithms
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comparison of predictive algorithms—such as Random Forest and LSTM neural networks—for use in genetic material recommendation systems. Working as part of Camcore’s data science team, the selected candidate
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grid analytics, and scientific imaging. The successful candidate will design and implement sparse algorithms for large-scale scientific and numerical computations. This role offers an exceptional
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, focusing on applications within the healthcare, education, and environment sectors. Designs generative AI techniques and algorithms for data integration and computational models, with objectives to amplify
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collected and writing up experiment results in the field of computational biology. Core responsibilities include: Working with biological data - implementing algorithms, programming and data analysis
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Summary Research Computing supports basic, translational, and clinical research by providing pediatric researchers with access to digital tools and technologies. We bring together a diverse set of data
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Driven Discovery. Job Responsibilities: Analyze biomedical data with minimal supervision by performing advanced analysis, algorithm implementation, programming, and quality check. Assist senior analysts
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. Experience: Experience in a research environment is highly preferred. Background and work knowledge in algorithms, scientific computing, and machine learning or statistics is preferred. Proficiency with C/C