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Computer Science at Princeton University, with the goal of developing next-generation machine-learningâ“based interatomic potentials (MLPs) for Earth and planetary materials across extreme pressureâ“temperature
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stakeholders involved in planning and decision-making. Qualifications Candidates must have a PhD in atmospheric physics, hydrology, meteorology, Earth system science, climate studies, applied mathematics
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mitigation, climate and energy, climate change, disease and health, climate justice, cities and the environment, earth history, ecology and ecosystem science, environmental chemistry and microbiology
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on solid-state defects is advantageous. Strong scientific communication skills, including peer-reviewed publications and presentations. Proven ability to work effectively in interdisciplinary teams spanning
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-level capability to perform all of the essential duties of the position in a safe, efficient, and responsible manner. Must possess the physical dexterity and stamina required to meet the physical demands
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: 285615321 Job Type Full-Time Overview Do you have a background in computational research and love to write code? Do you want to help enable and advance groundbreaking research in space physics? If so
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submission process and internal grant review cycle. Distribute approved grant funding from appropriate sources to faculty across campus. Monitor financial spending on internal grants. Sponsored research
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state of Princeton's decentralized fleet operations, identified operational gaps and risks, and outlined recommendations for establishing a coordinated, sustainable, and data driven fleet program. The
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to lead an investigation exploring the ability of recently developed global earth system models to simulate coastal sea level across sub-annual timescales. This work will leverage a suite of coupled models
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eligible) Pursuing a degree in the atmospheric or related sciences at a US accredited institution. Have a minimum grade point average of 3.0 on a 4.0-point scale. U.S. citizens or hold permanent resident