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Field
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developing LLM-based applications using Python APIs. Experience with large scale molecular dynamics (MD) packages e.g. lammps Experience with version control (e.g., Git) and collaborative software development
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. The project benefits from access to state-of-the-art HPC resources, including the MeluXina supercomputer, and from strong international collaborations, e.g., Max Planck Institute, Google DeepMind, TU Berlin
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considered. Connect with ORISE...on the GO! Download the new ORISE GO mobile app in the Apple App Store or Google Play Store to help you stay engaged, connected, and informed during your ORISE experience
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, agronomy, or a related field Desired Qualifications Strong skills in satellite remote sensing, GIS, and statistical analysis. Experience using cloud-based computing resources (e.g. Google Earth Engine
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Further information may be obtained from Carlos Acevedo-Rocha: cargac@ dtu.dk . Google Scholar profile . More information about BRIGHT: https://bright.dtu.dk , CPE group . If you are applying from abroad
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), multimodal vision and language models, and Large Language Models. Please find prior work here: (Google Scholar: https://scholar.google.com/citations?hl=en&user=oEifmSgAAAAJ&view_op=list_works&sortby=pubdate
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environments such as All of Us Research Program, Terra or Google Cloud Platform (GCP) Integrate multimodal datasets (EHR + genomics and other omics) Lead data cleaning, pipeline development, visualization, and
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particles are localized. For recent research outputs of the PIs, please see Andrei Bernevig’s entry at Google Scholar , and Päivi Törmä’s at Google Scholar . Your experience and ambitions We are looking for a
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for predictivemodelling and large foundation models for earth systems. Skillsetssuch as programming in Python/R, Google Earth Engine, JavaScript, database management environments, Geographical AI, and machine learning
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/leveraging large public databases (knowledge of working with public APIs) are advantages although not a must. Proficiency in Python and/or R in scientific computing and reproducible data analysis is a must