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Causal Inference Machine Learning Postdoctoral Jobs in Maryland

Umd

Senior Faculty Specialist

College Park, MD

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

Geico

Senior Applied Research Scientist

Bethesda, MD

$115K - $230K/yr

... expertise in machine learning and artificial intelligence. As the AI Research team at GEICO ... Experimentation & Causal Inference: Design online experiments and quasi-experimental analyses ...

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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.

Is it difficult to get a causal inference machine learning postdoctoral position?

Securing a causal inference machine learning postdoctoral position can be competitive due to specialized skills required, such as expertise in statistical methods, programming (e.g., Python or R), and a strong research background. Candidates with relevant publications, strong recommendations, and experience in machine learning frameworks often have better chances, but the availability of such positions varies by institution and funding.

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 August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 23% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

Postdoctoral Fellow (Civil and Systems Engineering)

Johns Hopkins University

Baltimore, MD • On-site

$48K - $66K/yr

Full-time

Re-posted 2 days ago

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8.0

Company rating: 8.0 out of 10

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Job description

Description
The Xu Lab in the Department of Civil and Systems Engineering at Johns Hopkins University is recruiting a postdoctoral researcher to work on problems at the intersection of natural hazards and AI. We are looking for a candidate interested in developing rigorous sensing frameworks and AI methods for complex human-infrastructure-environment systems, with applications to disaster risk, disaster response, and disaster recovery. The position is ideal for researchers who want to combine methodological innovation with real-world impacts.
Research in the lab focuses on generative modeling, causal inference, multimodal remote/in-situ/crowd sensing, LLMs for high-stake decision-making with applications on rapid disaster mapping and response, community resilience, and smart infrastructure systems. We welcome applicants from computer science, statistics, civil engineering, systems engineering, geoinformatics, and related fields. Experience in natural hazards, optimization, causal inference, foundation and generative models, remote sensing, multimodal sensing, infrastructure systems, or multi-agent LLMs is a plus.
The postdoctoral researcher is expected to:
• lead and collaborate on interdisciplinary research projects
• publish in top venues across machine learning and natural hazards domains
• mentor students and contribute to a collaborative research environment
This position offers substantial flexibility in shaping research direction while working on societally important problems with strong methodological depth.
Please attach the following materials:
• CV
• a brief statement of research interests
• contact information for references
• selected publications or writing samples
Qualifications
Doctoral degree in a related field.

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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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