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physical models. However, to achieve reliable results choosing the right methodology and training strategy is a large scientific challenge. Your job In this project, we aim to apply deep learning techniques
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Artificial Intelligence (AI), particularly in the development and application of Large Language Models (LLMs), to join our team working on predictive maintenance solutions. The ideal candidate will have
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of very large data sets, data analysis, and simulations of X-ray scattering and spectroscopy signatures of dynamic processes in battery materials. The theoretical/ simulation efforts are supported by
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benefits package. Required Qualifications: PHD in astronomy or physics. Preferred: Experience with large-scale structure data and software development. Special Instructions to Applicants: For full
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taxonomy in AI-assisted workflows Prototype and test automated classification scripts (Python/R) Document data pipelines and QA/QC procedures Supervision & Training Mentor PhD-level and undergraduate RAs
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demonstrated independent thinking and problem-solving abilities. Experience using large databases is preferred, and genetic data analysis skills are desirable. Required Qualifications PhD or MD in Public Health
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for projects examining questions in cardiovascular disease using extremely large data sets comprised of routinely-collected clinical data · Developing and maintaining requirements for access to Truveta Data and
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in this kind of environment will result in models too large to be handled and too instable to be solved. Data-driven approaches need to be used in addition to enrich the physics-based models
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RNA-seq data for testing and validation. Perform data analysis on large-scale RNA-seq data in pediatric cancer. This may involve the analysis of both scRNA-seq, bulk RNA-seq as well as new single-cell
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targets. You will take a lead on development of computational approaches to integrate multi-omics data from patient samples, including DNA methylation, histone modifications, single-cell transcriptomics and