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, including image acquisition, processing, analysis, and interpretation Develop and validate new imaging techniques, algorithms, or software to improve diagnostic accuracy and patient outcomes Collaborate with
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programming of algorithms. The use of programming languages such as Python, R, SQL, and C++ will be a daily part of the project, and proficiency in these languages is required. However, additional datasets will
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data analysis experts. The main tasks include the analysis of complex biomedical data using modern AI methods, as well as the development of novel machine and deep learning algorithms to understand
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algorithms will be developed to extract discriminative and predictive features from a multimodal dataset consisting of digital histopathological images, lung CT images, clinical, genomics, and multiproteomics
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development of resource algorithms for forecasting, and cross functional collaboration to provide a single source of truth for clinical trial resource demand and supply planning. What You’ll Do Resourcing
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drug experts (clinicians, clinician scientists, data scientists, and laboratory investigators) to co-develop phenotyping algorithms but is expected to serve as the domain expert in high-throughput
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active research interests in quantum computing, quantum algorithm/software development and applications in decarbonisation who can take full advantage of the unique opportunities the QDA provides
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attacks Develop and implement ML algorithms to identify vulnerabilities and predict potential threats in supply chain systems Prepare project deliverables and disseminate results through high-quality
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molecular targets critical for developing new therapies for rare diseases, based on genetic data and biological system simulations. -Computational Drug Repurposing: Developing novel algorithms and databases
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Nature Careers | Halifax Mid Harbour Nova Scotia Provincial Government, Nova Scotia | Canada | about 2 months ago
learning, statistics, algorithm engineering, bioinformatics, or advanced data visualization. Evidence of effective participation and leadership roles in interdisciplinary research teams, teaching, and