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Causal Inference Machine Learning Postdoctoral Jobs in Maryland

Umd

Senior Faculty Specialist

College Park, MD · On-site

Expertise in data analysis and machine learning, with experience applying these techniques in an educational context preferred. *Familiarity with experimental design and causal inference ...

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Causal Inference Machine Learning Postdoctoral information

What is a causal inference machine learning postdoctoral researcher?

A Causal Inference Machine Learning Postdoctoral researcher is a scientist who specializes in developing and applying machine learning methods to understand cause-and-effect relationships in data. They typically hold a recent PhD in statistics, computer science, economics, or a related field, and work in academic or industry research settings. Their work involves designing experiments, analyzing complex datasets, and creating models that can infer causal relationships, which are crucial for making robust predictions and informed decisions. This role often collaborates with interdisciplinary teams to apply these techniques to domains such as healthcare, social science, or economics.

What are the key skills and qualifications needed to thrive as a causal inference machine learning postdoctoral researcher?

To thrive as a Causal Inference Machine Learning Postdoctoral researcher, you need a strong background in statistics, causal inference methodologies, and advanced machine learning, usually evidenced by a PhD in a relevant field. Familiarity with programming languages such as Python or R, experience using statistical software (e.g., TensorFlow, PyTorch, Stan), and knowledge of causal inference libraries are typically required. Outstanding analytical thinking, problem-solving abilities, and strong communication skills help you collaborate effectively and explain complex concepts to diverse audiences. These skills and qualifications are vital for advancing research, deriving actionable insights from data, and contributing to impactful scientific discoveries.

What are some common challenges faced by causal inference machine learning postdoctoral researchers when integrating causal models with real-world data?

Causal Inference Machine Learning Postdoctoral researchers often encounter challenges such as dealing with unobserved confounding variables, ensuring data quality, and addressing biases inherent in observational datasets. Integrating advanced machine learning techniques with causal inference frameworks requires careful consideration of model assumptions and validation methods. Collaboration with domain experts is essential to properly interpret results and to translate findings into actionable insights, especially in interdisciplinary settings like healthcare or social sciences.

What is the difference between Causal Inference Machine Learning Postdoctoral vs Data Scientist?

AspectCausal Inference Machine Learning PostdoctoralData Scientist
Required CredentialsPhD in statistics, machine learning, or related fieldBachelor's or Master's in data science, computer science, or related field
Work EnvironmentAcademic research, research labs, universitiesCorporate, tech companies, startups
Industry UsageResearch, academia, specialized industry projectsBusiness analytics, product development, data-driven decision making
Common Search/ComparisonYesYes

The main difference is that Causal Inference Machine Learning Postdoctoral roles focus on academic research and developing new methods in causal inference, often requiring a PhD. Data Scientists typically work in industry, applying existing models to solve business problems, with a focus on data analysis and visualization. While both roles involve machine learning, the postdoctoral position emphasizes research and theory, whereas data science emphasizes practical application.

What are popular job titles related to Causal Inference Machine Learning Postdoctoral jobs in Maryland? For Causal Inference Machine Learning Postdoctoral jobs in Maryland, the most frequently searched job titles are:
What job categories do people searching Causal Inference Machine Learning Postdoctoral jobs in Maryland look for? The top searched job categories for Causal Inference Machine Learning Postdoctoral jobs in Maryland are:
Causal Inference Machine Learning Postdoctoral jobs near you
Infographic showing various Causal Inference Machine Learning Postdoctoral job openings in Maryland as of July 2026, with employment types broken down into 2% Locum Tenens, 86% Full Time, 11% Part Time, and 1% Contract. Highlights an 87% Physical, 1% Hybrid, and 12% Remote job distribution.

Postdoctoral Fellowship Position: Data Science and AI in Health and Medicine

Johns Hopkins University

Baltimore, MD • On-site

$48K - $66K/yr

Full-time

Posted 13 days ago

Johns Hopkins University rating

8.0

Company rating: 8.0 out of 10

Based on 71 frontline employees who took The Breakroom Quiz

183rd of 614 rated colleges and universities

Job description

Description
We invite applications for Postdoctoral Fellow positions in the broad areas of data science and AI, with a focus on developing and applying novel data science approaches, computational tools and statistical methods to advance health and biomedical research. Johns Hopkins University has recently made transformative new investment in launching a new Data Science and AI institute that will serve the hub for interdisciplinary data collaborations with faculties and students from across Johns Hopkins and will build the nation's foremost destination for emerging applications, opportunities and challenges presented by data science, machine learning and AI.
The Department of Biostatistics at Johns Hopkins University is internationally recognized as a leader in the development of statistical and computational methods for biomedical research. With over 30 full-time faculty and a thriving research community, the department plays a central role in advancing public health and clinical science through methodological innovation and deep interdisciplinary collaboration. Faculty members are actively engaged in a wide range of research areas, including large-scale health studies, computational biology, statistical genetics, neuroimaging, wearables, causal inference, and other emerging applications in data science and artificial intelligence (AI).
We are seeking outstanding candidates with strong foundations in statistics, computation, and applied data science. The successful applicant will have the opportunity to work with multiple faculty mentors in the department and potentially from other departments at JHU with research topics that may include-but are not limited to-computational approaches such as AI and machine learning; methodological foundations, computational approaches and biomedical applications, such as AI for biomedicine, causal inference, Bayesian inference, survival analysis, statistical genetics and genomics, electronic health records (EHRs), wearables, longitudinal or spatial data analysis, and other high-throughput data sources. Successful candidates will receive competitive salaries, as well as computing resources, travel support, and other benefits in accordance with departmental and university policies.
Candidates should indicate in their cover letter the names of potential faculty mentors and an indication of potential projects the candidate could work on that would advance our understanding and/or application of data science and AI to health and biomedical research. In describing potential projects, the candidates should not be afraid to propose directions that build on, but are distinct from, their work during PhD-we are interested as much in what your vision for future is as in your past accomplishments. Applicants whose work might bridge multiple faculty members' interests, or make connections across disparate fields, are particularly welcomed. Applicants are also encouraged to suggest ways in which their work can provide connections to other data science and AI work across the University, including the Data Science and AI Initiative.
Qualifications
  • PhD in Biostatistics, Statistics, Computer Science, or a related quantitative field
  • Strong methodological and computational skills
  • Demonstrated interest or experience in health-related applications of statistical or data science methods
  • Strong written and oral communication skills
  • Ability to work collaboratively with interdisciplinary teams

Application Instructions
Interested applicants should submit the following materials to Interfolio:
  • Cover letter (no more than 3 pages, including past research experience, names of potential faculty mentors in the department, a brief description of possible research projects and ways the candidate's work could help advance data science and AI in the Department and University, and the candidate's larger career goals)
  • Curriculum vitae
  • Contact information for three references

Applications will be reviewed beginning on October 1 and will be reviewed on a rolling basis until the positions are filled.
For more information about the Department of Biostatistics and its faculty, please visit https://publichealth.jhu.edu/departments/biostatistics . If you have any question about the position, please contact Mary Joy Argo (margo@jhu.edu).

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Gilman believed that teaching and research go hand in hand—that success in one depends on success in the other—and that a modern university must do both well. He also believed that sharing our knowledge and discoveries would help make the world a better place. In 145 years, we haven’t strayed from that vision. This is still a destination for excellent, ambitious scholars and a world leader in teaching and research. Distinguished professors mentor students in the arts and music, humanities, social and natural sciences, engineering, international studies, education, business, and the health professions. Those same faculty members, along with their colleagues at the university’s Applied Physics Laboratory, have made us the nation’s leader in federal research and development funding every year since 1979. That’s a fitting distinction for America’s first research university, a place that has revolutionized higher education in the U.S. and continues to bring knowledge and discoveries to the world.

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Headquarters location

Baltimore, MD, US

Year founded

1876

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