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Causal Inference Machine Learning Postdoctoral Jobs in Ithaca, NY

Howard

Assistant Professor

Locke, NY

Conduct high-quality, externally visible research in applied mathematics, machine learning, and ... Supervise and mentor undergraduate and graduate students, postdoctoral scholars, and junior ...

Varsity Tutors

LSAT Logical Reasoning Tutor

Ithaca, NY · Remote

$26 - $40/hr

... inference questions. Ability to explain argument structure, conditional logic, causal reasoning ... Ability to adapt to different learning styles and student needs. Ways To Connect With Students * 1 ...

All JobsCausal Inference Machine Learning Postdoctoral JobsCausal Inference Machine Learning Postdoctoral Jobs in Ithaca, NY

Causal Inference Machine Learning Postdoctoral information

See Ithaca, NY salary details

$34.2K

$52.3K

$58.8K

How much do causal inference machine learning postdoctoral jobs pay per year?

As of Aug 31, 2026, the average yearly pay for causal inference machine learning postdoctoral in Ithaca, NY is $52,293.00, according to ZipRecruiter salary data. Most workers in this role earn between $51,600.00 and $54,500.00 per year, depending on experience, location, and employer.

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.

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For Causal Inference Machine Learning Postdoctoral jobs in Ithaca, NY, the most frequently searched job titles are:

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Cities near Ithaca, NY with the most Causal Inference Machine Learning Postdoctoral job openings:

Causal Inference Machine Learning Postdoctoral jobs near you

Postdoctoral Fellow, MARA (Modeling, Abstraction and Reasoning Agents)

Ithaca, NY • On-site

Basis Research Institute
See more jobs at Basis Research Instituteexternal

$47K - $64K/yr

Full-time

Re-posted 4 days ago

Job description

Job Summary:
Basis Research Institute is a nonprofit applied AI research organization seeking a Postdoctoral Fellow for their MARA project. The role involves conducting research on foundational AI technologies, developing methods for modeling and reasoning, and engaging in knowledge transfer within the research community.
Responsibilities:
• Conduct independent and collaborative research focused on the MARA project.
• Develop new methods and algorithms for modeling, abstraction, and reasoning in AI systems.
• Apply these methods to concrete challenges such as the Abstract Reasoning Corpus (ARC) and other domains.
• Disseminate research findings through academic publications and presentations at leading conferences.
• Actively engage in knowledge transfer within Basis and Cornell University, converting research into actionable insights and algorithms.
• Provide mentorship to junior team members and contribute to the scientific discourse through seminars, workshops, and collaborative projects.
Qualifications:
Required:
• Researchers holding a PhD in computer science, artificial intelligence, machine learning, cognitive science, or related fields.
• Strong background in areas such as program synthesis, probabilistic programming, machine learning, AI reasoning systems, and cognitive modeling.
• Experience in developing AI systems that combine neural and symbolic methods is highly valued.
• Interest in foundational AI research and its applications to modeling, abstraction, and reasoning.
• Individuals with a demonstrated track record in scientific research, evidenced through publications, technical reports, or impactful software projects.
Company:
Basis is a nonprofit applied research organization with two mutually reinforcing goals. The first is to understand and build intelligence. Founded in 2022, the company is headquartered in New York, USA, with a team of 11-50 employees. The company is currently Early Stage.

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