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Causal Inference Machine Learning Postdoctoral Jobs in Texas

Apple

Applied Scientist

Austin, TX

$175K - $308K/yr

We are currently seeking an experienced and passionate Applied Scientist, who will work on innovative products at the intersection of causal inference, statistics, and machine learning to help ...

Apple

Applied Scientist

Austin, TX

$175K - $308K/yr

We are currently seeking an experienced and passionate Applied Scientist, who will work on innovative products at the intersection of causal inference, statistics, and machine learning to help ...

Apple

Applied Scientist

Austin, TX · On-site

We are currently seeking an experienced and passionate Applied Scientist, who will work on innovative products at the intersection of causal inference, statistics, and machine learning to help ...

LPL Financial

Sr. Analyst, Data Science

Austin, TX · On-site

$85K - $143K/yr

This is a high-impact, hands-on role for someone who wants to apply classical data science methods-machine learning, statistics, and causal inference-in a fast-moving, mission-driven environment. Key ...

Apple

Senior Data Scientist, Apple Ads

Austin, TX · On-site

... causal inference, and analytical problem solving Experience developing statistical, machine learning, econometric, forecasting, or optimization models to solve business problems Experience ...

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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 Texas? For Causal Inference Machine Learning Postdoctoral jobs in Texas, the most frequently searched job titles are:
What job categories do people searching Causal Inference Machine Learning Postdoctoral jobs in Texas look for? The top searched job categories for Causal Inference Machine Learning Postdoctoral jobs in Texas are:
What cities in Texas are hiring for Causal Inference Machine Learning Postdoctoral jobs? Cities in Texas with the most Causal Inference Machine Learning Postdoctoral job openings:
Causal Inference Machine Learning Postdoctoral jobs near you
Postdoctoral Fellow - Biostatistics

Postdoctoral Fellow - Biostatistics

MD Anderson

Houston, TX

$64K - $76K/yr

Full-time

Medical, Dental, Retirement, PTO

Posted 9 days ago

MD Anderson Cancer Center rating

8.4

Company rating: 8.4 out of 10

Based on 169 frontline employees who took The Breakroom Quiz

27th of 885 rated healthcare providers

Job description

The Department of Biostatistics at has one postdoctoral fellow position open for biostatistics and data science methodology research in clinical trials. The main focus is research and publication. The primary focus will be to develop novel methods for causal AI/inference methods, adaptive Bayesian clinical trial designs, derive related statistical theory, produce software for implementation, incorporate biomarkers in clinical trial design and analysis, and build statistical learning tools for large data sets. The postdoctoral fellow will work under the supervision of Dr. Liang on challenging and important clinical and biological projects that involve complex statistical modeling, data analysis, and computation.
All duties and responsibilities are carried out in compliance with institutional policies, ethical research standards, and applicable federal and state regulations.
LEARNING OBJECTIVES
Trainee will learn through various research projects, with a primary focus on: (1) developing novel statistical and data science methods, as well as user-friendly software, for integrating AI tools to evaluate novel treatments or design future clinical trials in overall population or subgroups, and (2) analyzing real-world and institutional medical datasets. A major methodological focus will be integrating machine learning/artificial intelligence tools, causal inference methods, Bayesian techniques, and adaptive designs to build innovative, next-generation tools for adaptively and efficiently evaluating treatment effectiveness and learning optimal treatment decisions that may vary by different patients' subgroups.
ELIGIBILITY REQUIREMENTS
Applicants must have a recent PhD in biostatistics or statistics from a reputed University/Institute or within 0-1 years of graduation. At least one first author publication in a peer reviewed journal stemming from PhD studies is required. Candidates must have strong methodological training in statistics or biostatistics, especially in causal inference or semiparametric methods, and have strong computer programming skills, in particular using R or Python. Expertise or skills in the following areas are highly desirable: Causal inference, double/debias machine learning, semiparametric methods, Bayesian MCMC computational methods, adaptive clinical trials, and machine learning for estimation or decision-making.
Please send CV and information on three referees directly to mliang2@mdanderson.org.
POSITION INFORMATION
MD Anderson offers full-time postdoc positions with a salary ranging from $64,000 to $76,000. depending on the number of years of postgraduate experience. The University of Texas MD Anderson Cancer Center offers excellent benefits, including medical, dental, paid time off, retirement, tuition benefits, educational opportunities, and individual and team recognition
Offsite work arrangements are subject to approval and may be modified or revoked at any time based on business needs, performance considerations, or regulatory requirements.
This position may be responsible for maintaining the security and integrity of critical infrastructure, as defined in Section 113.001(2) of the Texas Business and Commerce Code and therefore may require routine reviews and screening. The ability to satisfy and maintain all requirements necessary to ensure the continued security and integrity of such infrastructure is a condition of hire and continued employment.
It is the policy of The University of Texas MD Anderson Cancer Center to provide equal employment opportunity without regard to race, color, religion, age, national origin, sex, gender, sexual orientation, gender identity/expression, disability, protected veteran status, genetic information, or any other basis protected by institutional policy or by federal, state or local laws unless such distinction is required by law. http://www.mdanderson.org/about-us/legal-and-policy/legal-statements/eeo-affirmative-action.html

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