$85K/yr
Postdoctoral Positions: Perelman School of Medicine PostdoctoralLocation University of Pennsylvania - School of Medicine Open DateDescription Department: The Center for Causal Inference (CCI) in the ...
$85K/yr
Postdoctoral Positions: Perelman School of Medicine PostdoctoralLocation University of Pennsylvania - School of Medicine Open DateDescription Department: The Center for Causal Inference (CCI) in the ...
Member of Research Staff, Causal Inference, Voleon Securities Location Employment Type Full time ... and machine learning research as well as highly experienced finance and technology professionals.
Member of Research Staff, Causal Inference, Voleon Securities Location Employment Type Full time ... and machine learning research as well as highly experienced finance and technology professionals.
Yale University, Department of Biostatistics - Postdoctoral AssociateCompany Name Yale University ... learning, generative modeling, causal inference, foundation models or computational genomics.
Yale University, Department of Biostatistics - Postdoctoral AssociateCompany Name Yale University ... learning, generative modeling, causal inference, foundation models or computational genomics.
Causal Inference, External Controls, and Real-World Evidence Development of principled approaches ... Machine learning or causal prediction * Pediatric oncology Prior experience in pediatric oncology ...
Causal Inference, External Controls, and Real-World Evidence Development of principled approaches ... Machine learning or causal prediction * Pediatric oncology Prior experience in pediatric oncology ...
$160K - $190K/yr
Senior Data Scientist - Machine Learning & AI Senior Data Scientist - Machine Learning & AI Remote ... Perform statistical analysis, hypothesis testing, A/B testing, causal inference, and time-series ...
$160K - $190K/yr
Senior Data Scientist - Machine Learning & AI Senior Data Scientist - Machine Learning & AI Remote ... Perform statistical analysis, hypothesis testing, A/B testing, causal inference, and time-series ...
University of Michigan Department of Biostatistics - Postdoctoral Research Fellow Company Name ... AI and machine learning, deep learning and its statistical foundations, causal inference ...
University of Michigan Department of Biostatistics - Postdoctoral Research Fellow Company Name ... AI and machine learning, deep learning and its statistical foundations, causal inference ...
Responsibilities The postdoctoral fellow will work with Dr. Yi Li and his research group to develop ... AI and machine learning, deep learning and its statistical foundations, causal inference ...
Responsibilities The postdoctoral fellow will work with Dr. Yi Li and his research group to develop ... AI and machine learning, deep learning and its statistical foundations, causal inference ...
$207K - $218K/yr
Experience applying causal inference methodologies to both observational and experimental datasets. * An understanding of machine learning algorithms and techniques. * Strong programming skills ...
$207K - $218K/yr
Experience applying causal inference methodologies to both observational and experimental datasets. * An understanding of machine learning algorithms and techniques. * Strong programming skills ...
$150 - $200/hr
Experience applying causal inference methodologies to both observational and experimental datasets. * An understanding of machine learning algorithms and techniques. * Strong programming skills ...
$150 - $200/hr
Experience applying causal inference methodologies to both observational and experimental datasets. * An understanding of machine learning algorithms and techniques. * Strong programming skills ...
... causal framework development, statistical analysis, machine learning model development and ... Inference: Develop and apply causal inference methods, including experimental, econometric ...
... causal framework development, statistical analysis, machine learning model development and ... Inference: Develop and apply causal inference methods, including experimental, econometric ...
$182K - $228K/yr
Deep expertise in statistics, econometrics, machine learning and optimization with demonstrated application to pricing or prediction problems. Strong causal inference background including experiment ...
$182K - $228K/yr
Deep expertise in statistics, econometrics, machine learning and optimization with demonstrated application to pricing or prediction problems. Strong causal inference background including experiment ...
$200 - $250/hr
As a Principal Data Scientist Lead at Microsoft, you will set the long-term vision and standards for experimentation, causal inference, and machine learning across the organization. You will drive ...
$200 - $250/hr
As a Principal Data Scientist Lead at Microsoft, you will set the long-term vision and standards for experimentation, causal inference, and machine learning across the organization. You will drive ...
... causal inference. The position suits a candidate with prior experience using healthcare data ... Research Activities The postdoc researcher will: * Develop estimation strategies that use multiple ...
... causal inference. The position suits a candidate with prior experience using healthcare data ... Research Activities The postdoc researcher will: * Develop estimation strategies that use multiple ...
We use machine learning, causal inference, and measurement systems to synthesize Strava's unique data assets into models, metrics, and recommendations that our leadership team can act on with ...
New
We use machine learning, causal inference, and measurement systems to synthesize Strava's unique data assets into models, metrics, and recommendations that our leadership team can act on with ...
New
$200 - $250/hr
The Data Science & Analytics organization accelerates decision-making across product verticals using analytics, experimentation, causal inference, statistical modeling, and machine learning. This ...
$200 - $250/hr
The Data Science & Analytics organization accelerates decision-making across product verticals using analytics, experimentation, causal inference, statistical modeling, and machine learning. This ...
We use machine learning, causal inference, and measurement systems to synthesize Strava's unique data assets into models, metrics, and recommendations that our leadership team can act on with ...
We use machine learning, causal inference, and measurement systems to synthesize Strava's unique data assets into models, metrics, and recommendations that our leadership team can act on with ...
$63K/yr
... postdoctoral associate positions, starting immediately. Dr. Liu has extensive experience in big data analytics, systems biology, probabilistic graphical models, causal inference and machine learning.
$63K/yr
... postdoctoral associate positions, starting immediately. Dr. Liu has extensive experience in big data analytics, systems biology, probabilistic graphical models, causal inference and machine learning.
We use machine learning, causal inference, and measurement systems to synthesize Strava's unique data assets into models, metrics, and recommendations that our leadership team can act on with ...
New
We use machine learning, causal inference, and measurement systems to synthesize Strava's unique data assets into models, metrics, and recommendations that our leadership team can act on with ...
New
Platform inference work in a marketing context means building the causal machinery behind budget decisions: incrementality testing, geo experiments, media mix modelling and attribution that survives ...
Platform inference work in a marketing context means building the causal machinery behind budget decisions: incrementality testing, geo experiments, media mix modelling and attribution that survives ...
$200 - $250/hr
Additionally, you will advance quantitative methods by integrating causal inference, statistical modeling, and machine learning techniques to solve highly ambiguous and complex measurement challenges ...
New
$200 - $250/hr
Additionally, you will advance quantitative methods by integrating causal inference, statistical modeling, and machine learning techniques to solve highly ambiguous and complex measurement challenges ...
New
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?
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?
| Aspect | Causal Inference Machine Learning Postdoctoral | Data Scientist |
|---|---|---|
| Required Credentials | PhD in statistics, machine learning, or related field | Bachelor's or Master's in data science, computer science, or related field |
| Work Environment | Academic research, research labs, universities | Corporate, tech companies, startups |
| Industry Usage | Research, academia, specialized industry projects | Business analytics, product development, data-driven decision making |
| Common Search/Comparison | Yes | Yes |
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 are popular job titles related to Causal Inference Machine Learning Postdoctoral jobs in Kentucky?
For Causal Inference Machine Learning Postdoctoral jobs in Kentucky, the most frequently searched job titles are:
What job categories do people searching Causal Inference Machine Learning Postdoctoral jobs in Kentucky look for?
The top searched job categories for Causal Inference Machine Learning Postdoctoral jobs in Kentucky are:
What cities in Kentucky are hiring for Causal Inference Machine Learning Postdoctoral jobs?
Cities in Kentucky with the most Causal Inference Machine Learning Postdoctoral job openings:
Causal Inference Postdoctoral Researcher - Center for Causal Inference, University of Pennsylvania
On-site
$85K/yr
Other
Posted 9 days ago
Key responsibilities
develop new theory and methods in causal inference
develop software related to causal inference
participate in weekly working group meetings and the annual Causal Inference Summer Institute
Job description
University of Pennsylvania: Postdoctoral Positions: Perelman School of Medicine PostdoctoralLocation
#J-18808-LjbffrUniversity of Pennsylvania - School of Medicine
Open DateDescriptionDepartment: The Center for Causal Inference (CCI) in the Department of Biostatistics, Epidemiology & Informatics (DBEI)
This position is open to applications from US citizens and foreign nationals.
The Center for Causal Inference (CCI) is a research center in the Department of Biostatistics, Epidemiology and Informatics in the Perelman School of Medicine of the University of Pennsylvania (https://www.dbeicoe.med.upenn.edu/cci/about-us ) with close collaboration with the Wharton Statistics Department. The CCI is a multidisciplinary center that includes faculty, postdocs, and graduate students from Biostatistics, Statistics, Epidemiology, Criminology, Computer Science, Philosophy, and other disciplines. The mission of the CCI is to be the leading center for research and training in the development and application of causal inference theory and methods, with far-ranging applications in Health and Social Sciences.
The position is for a postdoctoral researcher in causal inference with considerable flexibility in terms of research focus, reflecting the interdisciplinary nature of the field. In particular, the postdoctoral researcher will have an opportunity to collaborate on a broad range of methodological projects under the mentorship of one or more faculty affiliated with CCI.
Sample content areas include:
- Developing methods to study the effect of antidiabetic treatments on the recurrence of hospitalizations for T2DM-related complications.
- Developing methods for constructing just-in-time adaptive interventions using mobile health (mHealth) data.
- Developing novel causal inference methods to reconcile possibly conflicting results between randomized trials or randomized trials and observational studies, particularly in the area of pregnancy research.
In addition, the researcher will receive mentorship for professional development from the CCI co-directors Professors Nandita Mitra and Eric Tchetgen Tchetgen, as well as dedicated mentorship based on project type, with the expectation that their time as a researcher will advance their career development.
Duties and Responsibilities: develop new theory and methods in causal inference; develop software; and potentially collaborate on applied projects in Health and Social Sciences. In addition, the fellow is expected to be an active member of the CCI, by, for example, participating in the weekly working group meeting and the annual Causal Inference Summer Institute.
QualificationsCandidates should have a doctoral degree in biostatistics, statistics, or a related field. Strong computational skills and expertise in causal inference are desired but not required. Start date and term are negotiable.
The University of Pennsylvania is an equal opportunity employer. Candidates are considered for employment without regard to race, color, sex, sexual orientation, religion, creed, national origin (including shared ancestry or ethnic characteristics), citizenship status, age, disability, veteran status or any class protected under applicable federal, state, or local law.