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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 ...

Umcp

Senior Faculty Specialist

Annapolis Junction, 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 methodologies ...

Umcp

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 methodologies ...

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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, and why are they important?

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
Postdoctoral Fellowship Opening: Applied Causal Inference for the Social and Behavioral Sciences

Postdoctoral Fellowship Opening: Applied Causal Inference for the Social and Behavioral Sciences

Johns Hopkins University

Baltimore, MD • On-site

$48K - $66K/yr

Full-time

This job post has expired today. Applications are no longer accepted.

Johns Hopkins Medicine rating

7.5

Company rating: 7.5 out of 10

Based on 205 frontline employees who took The Breakroom Quiz

232nd of 884 rated healthcare providers

Job description

Description
Postdoctoral fellowship opening to work on applied causal inference under the direction of Dr. Elizabeth Stuart, in collaboration with Dr. Beth McGinty and colleagues at Johns Hopkins and Weill Cornell. Projects will include policy evaluation methods and application, and methods of relevance for implementation science and the work of the ALACRITY Center for Health and Longevity in Mental Illness. Strong candidates also have a strong interest in teaching causal inference topics to broad audiences, including potential development of short courses and other trainings to introduce causal inference topics to individuals without a methodological background. Work will include methods development as well as applications of advanced statistical methods in public health and medicine, and will involve collaboration with other faculty in Biostatistics and the School of Public Health. A key focus of the work will be collaboration with researchers at Weill Cornell conducting evaluations of mental health policies and services.
Responsibilities will include statistical collaboration, methods development, methodological literature reviews, simulation studies, educational activities, data management and analyses, manuscript writing for journal publications, and presentations at scientific meetings. Individuals with training in quantitative methods, including Statistics, Biostatistics, Economics, Epidemiology, and Health Policy are welcome to apply. Knowledge of causal inference methods and experience with statistical software such as Stata or R is required. Applicants will join a collegial and interdisciplinary team, and communication and collaboration skills are highly valued.
Successful candidates will receive competitive salaries (in the range $65,000-$75,000), as well as computing resources, travel support, and other benefits in accordance with departmental and university policies. Application review will begin February 1, and applications will be considered until the position is filled. The position can start any time from April to September 2026. The initial appointment is for one year, with reappointment for a second year provided satisfactory performance.
Qualifications
PhD in biostatistics, statistics, economics, health policy, health economics, or other quantitative field
Application Instructions
Interested applicants should submit the following materials via Interfolio:
• Cover letter
• Curriculum vitae
• 2 reference letters
Questions about the position can be directed to Dr. Stuart (https://www.elizabethstuart.org/; estuart@jhu.edu).

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