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Job ID: 234523

Tenured or Tenure-track faculty positions focused on leveraging machine learning (ML) and artificial
Johns Hopkins University

Date Posted Apr. 12, 2024
Title Tenured or Tenure-track faculty positions focused on leveraging machine learning (ML) and artificial
University Johns Hopkins University
Baltimore, MD, United States
Department Environmental Health & Engineering
Application Deadline Open until filled
Position Start Date Available immediately
  • Core Faculty
  • Ecological and Environmental

General Description

The Johns Hopkins University's Department of Environmental Health and Engineering seeks applicants for tenured or tenure-track faculty positions focused on Leveraging machine learning (ML) and artificial intelligence (AI) tools for environmental engineering, science, and public health applications.  Candidates should have demonstrated excellence in research incorporating ML/AI, and strong fundamental training in environmental engineering/science, hydrology, atmospheric science, environmental chemistry/microbiology, or related discipline.  We are specifically seeking candidates in three thrust areas:

Systems Analysis and Optimization for Sustainable Water, Environment and Natural Resources Management: The challenges of future climate change, population growth, megadroughts and extreme events, water scarcity, decarbonization, and zero waste, require advanced systems analysis methodologies for managing complex human-natural coupled systems operating at the margins of sustainability.  The desired candidate will have a strong foundation in systems analysis and optimization, while leveraging modern tools such as machine learning/artificial intelligence, efficient statistical and stochastic scenario analysis, and life cycle analysis, to develop resilient and optimal management strategies for complex water, environmental and resource systems, during a time of unprecedented change.

Harnessing Big Data, Artificial Intelligence/Machine Learning/Deep Learning for Climate Change Adaptation and Resilience: Large volumes of historical simulations and future climate projections are available from a wide range of climate and environmental models.  Simultaneously, the abundance of data streams generated by satellite, airborne, and drone-based remote sensing and in-situ sensor systems holds enormous promise for real-time environmental monitoring.  Mining and extracting information from these big data streams requires overcoming challenges associated with scale issues, discoverability, analysis, interpretation.  We seek candidates whose research leverages modern data science tools, including Machine Learning and Artificial Intelligence, to integrate large multi-scale datasets and process-based models to inform environmental and resource management strategies for the 21st century.

Artificial Intelligence/Machine Learning Applications in Environmental Chemistry and Toxicology: Human and Planetary health is shaped by more than 100,000 chemical compounds that are in commercial use and released into the environment.  Protecting human health against exposures to these complex chemical mixtures and byproducts that result from their reactions, is a much greater challenge than quantifying the toxicity of specific chemicals.  There has been a recent growth in applications of machine learning to identify the chemical composition of complex mixtures.  Simultaneously, Machine Learning and Artificial Intelligence based computational toxicology methods are demonstrating promise in meeting the principles of the 3R's concept, especially replacement of animal testing.  We seek candidates with expertise in the use of AI/ML methods in computational environmental chemistry and toxicology, to identify chemicals of concern in complex environmental mixtures and predict their toxicity.

The Department of Environmental Health and Engineering, a cross-divisional department spanning the Whiting School of Engineering and the Bloomberg School of Public Health, has 49 tenured/tenure-track and 40 non-tenure-track faculty members. We embrace the vision outlined in the National Academy report Environmental Engineering for the 21st Century: Addressing Grand Challenges. We strive to make JHU the world leader in understanding how environmental change affects human health and welfare, and in finding solutions that improve the health of the planet, communities, and people, together as linked goals.  We invite candidates who share our vision to apply for this position. More information about the Department of Environmental Health and Engineering can be found at

The Whiting School of Engineering comprises over 200 full time tenure-track, research, and teaching-track faculty in nine academic programs with a total annual research budget of over $170 million. The Bloomberg School of Public Health is the number 1 ranked public health school in the country with over 670 full time tenure-track faculty in 10 academic departments with an annual budget of over $500 million. Research partnerships with the Johns Hopkins School of Medicine, Applied Physics Laboratory, and the Krieger School of Arts and Sciences make the Whiting School of Engineering a unique research and educational environment. Opportunities exist for collaboration with several institutes and centers at JHU, including the new Data Science and AI Institute, the Ralph S. O'Connor Sustainable Energy Institute (ROSEI), the Center for a Livable Future (CLF), the CHARMED center, and the Center for Alternatives to Animal Testing (CAAT).  Student enrollment exceeds 1800 at the undergraduate level with over 1000 full time MS and PhD students. The Engineering for Professionals program enrolls over 2500 part time continuing education students and is the largest program of its kind in the country.  The Whiting School of Engineering is in the top 20 for both undergraduate programs and graduate school rankings by US News and World Report.

  • Equal Employment Opportunity Statement

    The Johns Hopkins University is committed to equal opportunity for its faculty, staff, and students. To that end, the university does not discriminate on the basis of sex, gender, marital status, pregnancy, race, color, ethnicity, national origin, age, disability, religion, sexual orientation, gender identity or expression, veteran status or other legally protected characteristic. The university is committed to providing qualified individuals access to all academic and employment programs, benefits and activities on the basis of demonstrated ability, performance and merit without regard to personal factors that are irrelevant to the program involved.

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Contact Information

  • Dr. Benjamin Hobbs
    Environmental Health & Engineering
    Johns Hopkins University
    Baltimore, MD


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