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Causal Inference Machine Learning Postdoctoral Jobs in Brockport, NY

L3HHCM20

Intern , Artificial Intelligence

Rochester, NY · On-site +1

$22.50 - $41/hr

Experience developing models using classical and SotA Machine Learning * Experience maintaining training and inference infrastructure for ML models PAY TRANSPARENCY: In compliance with pay ...

New

Pwc

AI Engineer

Rochester, NY

$50K - $112K/yr

Certifications aligned to data engineering, machine learning, and cloud platforms, including AWS ... using quantization, inference acceleration, and model-routing techniques - Designing agent ...

Varsity Tutors

LSAT Logical Reasoning Tutor

Rochester, 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 ...

L3Harris

Intern , Artificial Intelligence

Rochester, NY · On-site

$22.50 - $41/hr

Experience developing models using classical and SotA Machine Learning * Experience maintaining training and inference infrastructure for ML models PAY TRANSPARENCY: In compliance with pay ...

New

MegazoneCloud

Sr. Solutions Architect AI

Rochester, NY · On-site

$170K - $195K/yr

... time inference. * Own the Ecosystem: Drive strategic relationships with AWS, Azure, GCP, and ... Relevant high-level certifications (e.g., Databricks Machine Learning Professional, AWS ML ...

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

Causal Inference Machine Learning Postdoctoral information

See Brockport, NY salary details

$32.8K

$50.1K

$56.4K

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

As of Aug 30, 2026, the average yearly pay for causal inference machine learning postdoctoral in Brockport, NY is $50,131.00, according to ZipRecruiter salary data. Most workers in this role earn between $49,500.00 and $52,200.00 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.

What cities near Brockport, NY are hiring for Causal Inference Machine Learning Postdoctoral jobs?

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

Causal Inference Machine Learning Postdoctoral jobs near you
Infographic showing various Causal Inference Machine Learning Postdoctoral job openings in Brockport, NY as of August 2026, with employment types broken down into 1% As Needed, 71% Full Time, 25% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $50,131 per year, or $24.1 per hour.

Tenure-Track Assistant Professor

Rochester, NY • On-site

University of Rochester
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Learn more about University of Rochesterexternal

Full-time

Posted 4 days ago

Job description

Description
The Department of Biostatistics and Computational Biology (DBCB) at the University of Rochester (UR) is seeking highly qualified applicants for a tenure-track assistant professor position. The DBCB has a strong preference for attracting applicants with dual interests in the development of statistical/computational methodology and collaborative biomedical research; successful candidates will possess the research skills and experience required to become leaders in both dimensions. Although applications are welcomed from all corners of biostatistics and will be reviewed carefully, ideal candidates will have expertise and interests in: clinical trial design, including Bayesian adaptive design; gene-trait association studies; omics-informed precision medicine; spatial transcriptomics and/or functional genomics. Each of these areas offers strong opportunities for collaboration with faculty across the UR Medicine health system, and either aligns with or complements departmental strengths. Training or expertise in related areas such as casual inference, Bayesian inference, high-dimensional data analysis, machine learning, data integration and data visualization are highly desirable.
The DBCB currently has 20 faculty members, and 2 active emeritus, with methodological research interests collectively spanning a broad cross-section of topics in biostatistics and statistics; examples include clinical trial design (including adaptive and SMART trials), causal inference, longitudinal data, survival analysis, Bayesian methods, analysis of social and biological network data, systems biology, and the analysis of large-scale data sets (e.g., from high-throughput genome sequencing and imaging applications). DBCB faculty also collaborate with over 30 departments and centers, including the Departments of Biomedical Genetics, Medicine, Neurology, Pediatrics and Psychiatry; the Eastman Institute for Oral Health; the NCI-designated Wilmot Cancer Institute; the Center for Health + Technology; the Environmental Health Sciences Center; the Genomics Research Center; and, the Clinical and Cardiovascular Research Center. These collaborations provide a rich source of data and pose novel methodological challenges. Many faculty hold joint appointments in the departments or centers in which they collaborate. The Department offers a highly regarded PhD program in Statistics; it also runs two master's degree programs. Many graduates of these programs have gone on to distinguished careers in academia or industry. The DBCB also supports several postdoctoral fellows, master's-level statisticians and programmers.
Qualifications
Doctoral degree in biostatistics, statistics, or a strongly related discipline with equivalent training. In addition to strong potential for research, successful candidates will have excellent oral and written communication skills and high enthusiasm for graduate teaching and advising.
Application Instructions
If you already have an Interfolio account, please sign in to apply to this position. If not, please create an Interfolio account. For questions/concerns pertaining to the position, email Stephanie_Johnson2@URMC.Rochester.edu.
The referenced pay range represents the University's good faith and reasonable estimate of the base range of compensation for this faculty position. Individual salaries will be determined within the job's salary range and established based on (but not limited to) market data, experience and expertise of the individual, and with consideration to related position salaries. Alignment of clinical incentive-based compensation may also be applicable and will be discussed during the hiring process.

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