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prostate, bladder and kidney cancer). The program will draw on expertise in probabilistic modeling and causal machine learning, phylogenetic and evolutionary inference, clonal dynamics, and immune-tumour
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 5 days ago
-series. Experience exploring machine learning and deep learning techniques for geospatial applications is highly desirable to effectively engage with Earth observation foundation models. Technical
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, microplastics; by means of numerical and experimental approaches including high-performance computing, data science methods via machine learning/AI and digital twins, enhancement, development and of application
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Engineering, Bioinformatics, Statistics, Applied Mathematics, Physics, or a closely related STEM field. Demonstrated experience developing AI and machine learning models for biomedical applications. Job
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world and a suite of associated machine learning tools. The incumbent will be advised by Dr. Laurel Symes (CAPS, [email protected]). Depending on the research direction, collaboration and additional
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flexible learning models. Strong understanding of education pathways, stackable credentials, and learner progression. Ability to collaborate on learner recruitment and outreach efforts in partnership with
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support for the lab Job Requirements: Bachelor’s or Master’s degree in Electrical Engineering, Computer Science, or related field Knowledge and experience in world model, computer vision and deep learning
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machine-learning approaches. Established markers such as neurofilament light chain (NfL) and GFAP will provide a biological reference point for identifying disease-specific biomarkers. A central part of the
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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Are you interested in working with nuclear fuel modelling, machine learning
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atomistic simulations, scientific machine learning, reaction modeling, and integration of computational and experimental data. The associate will develop reproducible computational workflows, collaborate with