{"schemaVersion":"jobsearcher.job.v1","id":"b8e35d9810b389bc5f7dbb4e","url":"https://jobsearcher.com/jobs/b8e35d9810b389bc5f7dbb4e","canonicalUrl":"https://jobsearcher.com/jobs/b8e35d9810b389bc5f7dbb4e","title":"Postdoctoral Associate - Earth Environmental Planetary Sciences","description":"Position Summary\n\nDr. Ian McBrearty’s lab in the Department of Earth, Environmental and Planetary Sciences is looking to hire a Postdoctoral Research Associate in the field of Machine Learning & Geophysics.\n\nThe McBrearty Lab sits at the intersection of machine learning and physics, focusing on developing data-driven techniques for earthquake monitoring, processing data from large seismic networks, and developing neural-surrogate emulations of PDEs governing tectonic and volcanic processes.\n\nThe ideal candidate will hold a Ph.D. in a quantitative field with strong Python and deep learning skills to build data-driven tools for earthquake detection and geophysical forecasting. They will be responsible for developing graph neural networks (GNNs), advancing PDE emulation methods, publishing high-impact research, and utilizing high-performance computing resources. Review of applications begins September 1, 2026, and will continue until the position is filled. Informal inquiries can be sent to Dr. Ian McBrearty at im76@rice.edu.\n\nWorkplace Requirements:\n\nOn campus position: This position is exclusively on-site, necessitating all duties to be performed in-person in Houston, Texas. Per Rice policy 440 , work arrangements may be subject to change.\n\n*Exempt (salaried) positions under FLSA are not eligible for overtime.\n\nThis position is funded by a grant, soft and/or restricted funds. Continued employment is contingent on the renewal of funding.\n\nProposed Salary: $65,000\n\nEssential Functions\n\nDevelops and deploys machine learning models (specifically graph neural networks) to process large, spatially irregular seismic datasets and advance neural-surrogate emulations of PDEs governing geophysical processes\n\nDocuments, analyzes, and maintains research data\n\nPublishes and presents research findings\n\nSupports project management and collaboration across institutions or disciplines\n\nPerforms all other duties as assigned\n\nRequired Qualifications and Skills\n\nPh.D. in Geophysics, Computer Science, Data Science, Applied Mathematics, or a related quantitative field\n\nStrong Python programming skills and practical experience with deep learning frameworks (e.g., PyTorch, TensorFlow) for scientific data analysis\n\nExcellent verbal and written communication skills, as well as oral presentation skills\n\nOrganization and time management skills\n\nKnowledge of modern research methods, data collection, and analyses\n\nAbility to write scholarly papers based on ongoing research in order to submit them to journals for publication\n\nAble to work in a collaborative environment\n\nAble to work independently and professionally with minimal super vision and direction\n\nPreferred Qualifications\n\nExperience with Graph Neural Networks (GNNs) or physics-informed machine learning (PINNs).\n\nBackground in seismological software packages (e.g., ObsPy) and large-scale, high-performance computing (HPC) data processing.\n\nRice University HR | Benefits: https://knowledgecafe.rice.edu/benefits\n\nRice Mission and Values : Mission and Values | Rice University\n\nRice University is committed to ensuring Equal Employment Opportunity and welcoming the fullness of diversity into our candidate pools. Rice considers qualified applicants for employment without regard to race, color, religion, age, sex, sexual orientation, gender identity, national or ethnic origin, genetic information, disability, or protected veteran status. Rice also provides reasonable accommodations to qualified persons with disabilities. If an applicant requires a reasonable accommodation for any part of the application or hiring process, please get in touch with Rice University’s Human Resources Office via email at facstaffada@rice.edu for support.\n\nIf you have any additional questions, please email us at jobs@rice.edu . Thank you for your interest in employment with Rice University","company":"Rice Engineering","rawCompany":"rice engineering","city":"Houston","state":"TX","isRemote":false,"isActive":false,"createdAt":"2026-08-24T09:07:27.811Z","occupations":[{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"},{"code":"19-2042.00","title":"Geoscientists, Except Hydrologists and Geographers","slug":"geoscientists-except-hydrologists-and-geographers"},{"code":"19-2099.00","title":"Physical Scientists, All Other","slug":"physical-scientists-all-other"}],"industries":[{"code":"611310","title":"Colleges, Universities, and Professional Schools","slug":"colleges-universities-and-professional-schools"},{"code":"541715","title":"Research and Development in the Physical, Engineering, and Life Sciences (except Nanotechnology and Biotechnology)","slug":"research-and-development-in-the-physical-engineering-and-life-sciences-except-nanotechnology-and-biotechnology"},{"code":"541690","title":"Other Scientific and Technical Consulting Services","slug":"other-scientific-and-technical-consulting-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Postdoctoral Associate - Earth Environmental Planetary Sciences","description":"Position Summary\n\nDr. Ian McBrearty’s lab in the Department of Earth, Environmental and Planetary Sciences is looking to hire a Postdoctoral Research Associate in the field of Machine Learning & Geophysics.\n\nThe McBrearty Lab sits at the intersection of machine learning and physics, focusing on developing data-driven techniques for earthquake monitoring, processing data from large seismic networks, and developing neural-surrogate emulations of PDEs governing tectonic and volcanic processes.\n\nThe ideal candidate will hold a Ph.D. in a quantitative field with strong Python and deep learning skills to build data-driven tools for earthquake detection and geophysical forecasting. They will be responsible for developing graph neural networks (GNNs), advancing PDE emulation methods, publishing high-impact research, and utilizing high-performance computing resources. Review of applications begins September 1, 2026, and will continue until the position is filled. Informal inquiries can be sent to Dr. Ian McBrearty at im76@rice.edu.\n\nWorkplace Requirements:\n\nOn campus position: This position is exclusively on-site, necessitating all duties to be performed in-person in Houston, Texas. Per Rice policy 440 , work arrangements may be subject to change.\n\n*Exempt (salaried) positions under FLSA are not eligible for overtime.\n\nThis position is funded by a grant, soft and/or restricted funds. Continued employment is contingent on the renewal of funding.\n\nProposed Salary: $65,000\n\nEssential Functions\n\nDevelops and deploys machine learning models (specifically graph neural networks) to process large, spatially irregular seismic datasets and advance neural-surrogate emulations of PDEs governing geophysical processes\n\nDocuments, analyzes, and maintains research data\n\nPublishes and presents research findings\n\nSupports project management and collaboration across institutions or disciplines\n\nPerforms all other duties as assigned\n\nRequired Qualifications and Skills\n\nPh.D. in Geophysics, Computer Science, Data Science, Applied Mathematics, or a related quantitative field\n\nStrong Python programming skills and practical experience with deep learning frameworks (e.g., PyTorch, TensorFlow) for scientific data analysis\n\nExcellent verbal and written communication skills, as well as oral presentation skills\n\nOrganization and time management skills\n\nKnowledge of modern research methods, data collection, and analyses\n\nAbility to write scholarly papers based on ongoing research in order to submit them to journals for publication\n\nAble to work in a collaborative environment\n\nAble to work independently and professionally with minimal super vision and direction\n\nPreferred Qualifications\n\nExperience with Graph Neural Networks (GNNs) or physics-informed machine learning (PINNs).\n\nBackground in seismological software packages (e.g., ObsPy) and large-scale, high-performance computing (HPC) data processing.\n\nRice University HR | Benefits: https://knowledgecafe.rice.edu/benefits\n\nRice Mission and Values : Mission and Values | Rice University\n\nRice University is committed to ensuring Equal Employment Opportunity and welcoming the fullness of diversity into our candidate pools. Rice considers qualified applicants for employment without regard to race, color, religion, age, sex, sexual orientation, gender identity, national or ethnic origin, genetic information, disability, or protected veteran status. Rice also provides reasonable accommodations to qualified persons with disabilities. If an applicant requires a reasonable accommodation for any part of the application or hiring process, please get in touch with Rice University’s Human Resources Office via email at facstaffada@rice.edu for support.\n\nIf you have any additional questions, please email us at jobs@rice.edu . Thank you for your interest in employment with Rice University","datePosted":"2026-08-24T09:07:27.811Z","dateModified":"2026-08-24T09:07:27.811Z","hiringOrganization":{"@type":"Organization","name":"Rice Engineering","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Houston","addressRegion":"TX","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"b8e35d9810b389bc5f7dbb4e"},"url":"https://jobsearcher.com/jobs/b8e35d9810b389bc5f7dbb4e"}}