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Causal Inference Machine Learning Postdoctoral Jobs in Ithaca, NY

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

... 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 ...

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 Jul 20, 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, 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 Ithaca, NY? For Causal Inference Machine Learning Postdoctoral jobs in Ithaca, NY, the most frequently searched job titles are:
What job categories do people searching Causal Inference Machine Learning Postdoctoral jobs in Ithaca, NY look for? The top searched job categories for Causal Inference Machine Learning Postdoctoral jobs in Ithaca, NY are:
What cities near Ithaca, NY are hiring for Causal Inference Machine Learning Postdoctoral jobs? Cities near Ithaca, NY with the most Causal Inference Machine Learning Postdoctoral job openings:
Assistant Professor

Full-time

Medical, Dental, Vision, Retirement, PTO

Re-posted 16 hours ago


Job description

The Talent Acquisition department hires qualified candidates to fill positions which contribute to the overall strategic success of Howard University. Hiring staff "for fit" makes significant contributions to Howard University's overall mission.

At Howard University, we prioritize well-being and professional growth.

Here is what we offer:

  • Health & Wellness: Comprehensive medical, dental, and vision insurance, plus mental health support
  • Work-Life Balance: PTO, paid holidays, flexible work arrangements
  • Financial Wellness: Competitive salary, 403(b) with company match
  • Professional Development: Ongoing training, tuition reimbursement, and career advancement paths
  • Additional Perks: Wellness programs, commuter benefits, and a vibrant company culture

Join Howard University and thrive with us!

https://hr.howard.edu/benefits-wellness

The Department of Mathematics in the College of Arts and Sciences at Howard University invites applications for a full-time, tenure-track Assistant or Associate Professor position. The department seeks candidates with research expertise and teaching interests in mathematics enhanced artificial intelligence (AI), with applications to radiology, endocrinology, and related biomedical domains. The anticipated start date is August 2026.
This position is part of the Provost's Artificial Intelligence Cluster Hire Initiative and is embedded within the interdisciplinary cluster "Advancing Health through Mathematics-Enhanced AI for Radiology and Endocrinology", jointly led by the College of Arts and Sciences and the College of Medicine. The cluster aims to strengthen university-wide research capacity by recruiting faculty whose scholarship advances mathematical foundations, modeling, and computational methodologies that support AI-driven biomedical discovery, while fostering
collaboration across disciplines.


SUPERVISORY AUTHORITY: The successful candidate is expected to demonstrate a strong commitment to excellence in teaching, mentoring, and service. The faculty member will contribute to the growth of the Department of Mathematics' research and academic programs in data science, applied mathematics, and AI, and will actively participate in interdisciplinary initiatives across the university.


NATURE AND SCOPE:
From a mathematics-centered perspective, this position emphasizes the development and application of mathematical theory, statistical modeling, and computational methods that underpin modern AI and machine learning approaches in biomedical sciences. The cluster seeks to advance quantitative frameworks for imaging, inference, prediction, and decision-making in complex biological and clinical systems. The successful candidate will contribute to an interdisciplinary academic environment through research, teaching, mentorship, and collaboration. Responsibilities include leading independent and collaborative research programs at the interface of mathematics, AI, and biomedical
imaging; contributing to undergraduate and graduate curriculum development; mentoring students; and conducting research that addresses health disparities and promotes inclusive excellence.

PRINCIPAL ACCOUNTABILITIES:
Conduct high-quality, externally visible research in applied mathematics, machine learning, and artificial intelligence, with relevance to biology and medicine.
Develop and sustain an externally funded research program, including collaborative interdisciplinary projects.
Teach undergraduate and graduate courses in mathematics, data science, machine learning, and artificial intelligence.
Supervise and mentor undergraduate and graduate students, postdoctoral scholars, and junior researchers.
Collaborate with faculty across mathematics, medicine, and related disciplines to advance AI-enabled quantitative methods for biomedical applications.

CORE COMPETENCIES:
The Department of Mathematics particularly encourages applicants with expertise in one or more of the following areas:
Mathematical Foundations of AI and Machine Learning:
Optimization, statistical learning theory, inverse problems, uncertainty quantification, dynamical systems, or computational mathematics relevant to AI.
AI and Data-Driven Modeling in Biomedicine:
Development of mathematical and computational methods for imaging, neuroscience, endocrinology, diabetes, or other data-intensive biomedical domains.
Interdisciplinary Research and Collaboration:
Demonstrated ability to collaborate with clinicians, biologists, or engineers to develop mathematically grounded AI/ML approaches for complex biological and medical datasets.

MINIMUM REQUIREMENTS:
Ph.D. in Mathematics, Applied Mathematics, Computer Science, Data Science, Artificial Intelligence, or a closely related field.
Strong research record in mathematics-driven AI or ML, with applications to biology or medicine, evidenced by peer-reviewed publications.
Demonstrated ability or clear potential to secure external research funding.
Evidence of excellence or strong potential in undergraduate and graduate teaching.
Commitment to interdisciplinary collaboration, student mentorship, and advancing diversity, equity, and inclusion in teaching and research.

Compliance Salary Range Disclosure

Compensation Range: $135,000 - $150,000