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Causal Inference Machine Learning Postdoctoral Jobs in Maryland

Johns Hopkins University

Postdoctoral Fellow

Baltimore, MD · On-site

$48K - $66K/yr

Machine learning / artificial intelligence (using imaging or 'omics data) Responsibilities: * Lead ... Postdoctoral fellows receive comprehensive benefits; see JHU Postdoctoral Fellow Benefits Overview ...

Marylandconnect

We especially welcome candidates whose research employs modern causal inference methods, machine learning, or other data-intensive empirical techniques. The successful candidatewill be expected ...

Gormat

Data Scientist 2

Annapolis Junction, MD · On-site

$99K - $114K/yr

* We are seeking a Data Scientist with a background in AI, Machine Learning (ML), and Natural ... Modeling, Inference, and Prediction: (Data modeling and assessment, domain-specific considerations)

Gormat

Data Scientist 2

Annapolis Junction, MD

* We are seeking a Data Scientist with a background in AI, Machine Learning (ML), and Natural ... Modeling, Inference, and Prediction: (Data modeling and assessment, domain-specific considerations)

Digital Harbor Foundation

Research Scientist

Baltimore, MD · On-site +1

$120K - $150K/yr

... postdoctoral or equivalent research experience applying machine learning or deep learning to ... Familiarity with uncertainty quantification methods (e.g., ensembles, Bayesian inference) and ...

Gormat

Data Scientist 2

Annapolis Junction, MD · On-site

$99K - $114K/yr

* We are seeking a Data Scientist with a background in AI, Machine Learning (ML), and Natural ... Modeling, Inference, and Prediction: (Data modeling and assessment, domain-specific considerations)

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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 Fellow

Postdoctoral Fellow

Johns Hopkins University

Baltimore, MD • On-site

$48K - $66K/yr

Full-time

Re-posted 22 days ago

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Job description

Description
The Cho Laboratory directed by Sangkyun Cho, PhD, at Johns Hopkins University seeks 1-2 postdoctoral fellows to lead research projects at the intersection of molecular/cellular biology, tissue engineering, and multi-omics, with an emphasis on fibrosis and precision therapeutics. We are especially interested in candidates who bring deep domain expertise in one or more of the following areas:
  • Stem cells, organoids, tissue engineering, and/or 3D bioprinting
  • Biomaterials for regenerative medicine and mechanobiology
  • CRISPR genome editing, CRISPRa/i
  • Animal work and rodent surgeries
  • Machine learning / artificial intelligence (using imaging or 'omics data)

Responsibilities:
  • Lead independent and collaborative projects; establish new capabilities for the group
  • Design and execute experiments/analyses; maintain rigorous documentation and data stewardship.
  • Mentor and train graduate and undergraduate students; foster an inclusive culture and lab environment.
  • Prepare manuscripts, present at conferences, and contribute to proposals.

Qualifications
Required
  • Ph.D. (or equivalent) in Biomedical Engineering, Bioengineering, Chemical Engineering, Molecular/Cellular Biology, or a related field by start date.
  • Demonstrated expertise in at least one target area listed above, evidenced by publications, patents, and/or tool development.
  • Strong record of teamwork, communication, and research rigor.

Preferred
  • Prior experience setting up or running complex workflows (e.g., organoid platforms, rodent surgeries, high-throughput screening, ML pipelines, etc.).
  • Experience mentoring students and coordinating multi-lab collaborations.
  • Familiarity with induced pluripotent stem cells (iPSCs), cardiovascular physiology, and single-cell/spatial omics.

The Cho Lab is part of the Institute for NanoBioTechnology (INBT), home to a vibrant community of researchers working at the interface of engineering, biology, nanoscience, and medicine. Candidates will collaborate closely with experts across INBT and the broader Homewood Campus, as well as those at the Johns Hopkins School of Medicine, Bloomberg School of Public Health, and the Center for Microphysiological Systems (MPS).
Application Instructions
  • How to apply (Interfolio): Submit (i) latest CV, (ii) cover letter with a description of research background and interests, and (iii) names and contact info for three references.
  • Benefits: Postdoctoral fellows receive comprehensive benefits; see JHU Postdoctoral Fellow Benefits Overview.
  • Johns Hopkins University is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration without regard to race, color, religion, sex, sexual orientation, gender identity or expression, age, national origin, disability, protected veteran status, or any other status protected by law; reasonable accommodations are available.
  • Background checks and employment eligibility verification may be required per University policy.

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