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Postdoctoral Position In Control Optimization Jobs

Duke University

POSTDOCTORAL ASSOCIATE

Durham, NC · On-site

... postdoctoral position if the candidate has completed all of the requirements for a degree but the degree has not been formally conferred: in this case, the candidate may present evidence of ...

The Pennsylvania State University

POSITION SPECIFICS A postdoctoral position is available immediately in the Laboratory of Dr ... Santhosh Girirajan in the Department of Biochemistry and Molecular Biology at Penn State to study ...

Swarthmore College

Postdoctoral Researcher

Swarthmore, PA · On-site

$65K - $71K/yr

We invite applications for a postdoctoral position in tissue biomechanics to work in the group of Associate Prof. Eva-Maria Collins at Swarthmore College. The Collins lab is an interdisciplinary ...

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Postdoctoral Position In Control Optimization information

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How much do postdoctoral position in control optimization jobs pay per year?

As of Jun 7, 2026, the average yearly pay for postdoctoral position in control optimization in the United States is $59,022.00, according to ZipRecruiter salary data. Most workers in this role earn between $49,000.00 and $66,500.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Postdoctoral Researcher in Control Optimization, and why are they important?

To thrive in a Postdoctoral Position in Control Optimization, you need a PhD in control engineering, applied mathematics, or a related field, with strong expertise in optimization theory and systems control. Familiarity with technical tools like MATLAB, Python, and specialized simulation or optimization libraries, as well as experience with numerical methods, is typically required. Strong analytical thinking, problem-solving abilities, and clear scientific communication help you excel in collaborative research environments. These skills are crucial for advancing cutting-edge research, publishing impactful results, and contributing innovative solutions to complex control problems.

What is the difference between Postdoctoral Position In Control Optimization vs Postdoctoral Position In Robotics?

AspectPostdoctoral Position In Control OptimizationPostdoctoral Position In Robotics
Required CredentialsPhD in Control Systems, Electrical Engineering, or related fieldsPhD in Robotics, Mechanical Engineering, or related fields
Work EnvironmentResearch labs focusing on control algorithms and system optimizationResearch labs working on robotic systems, automation, and mechanical design
Employer & Industry UsageUniversities, research institutes, industries developing control solutions

While both roles require a PhD and involve research in engineering, the Postdoctoral Position In Control Optimization primarily focuses on developing and analyzing control algorithms for dynamic systems. In contrast, the Postdoctoral Position In Robotics emphasizes designing and testing robotic systems, often integrating control methods. Both roles share overlapping skills but differ in their application focus and industry context.

What are some common challenges faced in a Postdoctoral Position in Control Optimization, and how can candidates prepare for them?

Postdoctoral researchers in control optimization often encounter challenges such as integrating complex mathematical models with real-world engineering systems, adapting to rapidly evolving research objectives, and managing interdisciplinary collaboration. To prepare, candidates should develop strong problem-solving skills, maintain flexibility in their research approach, and actively seek opportunities to learn from colleagues in related fields. Regular communication with supervisors and team members is crucial for successfully navigating these challenges and ensuring progress toward research goals.

What is a postdoctoral position in control optimization?

A postdoctoral position in control optimization is a temporary research role typically held after earning a PhD. The focus is on developing and analyzing advanced algorithms and techniques to optimize control systems, which are used in fields like robotics, automation, and engineering. Postdocs in this area work on both theoretical and applied aspects, often publishing papers, attending conferences, and collaborating with faculty and industry partners. These positions help researchers gain further expertise and prepare for academic or industry careers.
Infographic showing various Postdoctoral Position In Control Optimization job openings in the United States as of May 2026, with employment types broken down into 77% Full Time, 6% Part Time, and 17% Contract. Highlights an 78% In-person, and 22% Remote job distribution, with an average salary of $59,022 per year, or $28.4 per hour.
Postdoctoral Research Position in Causal Inference

Postdoctoral Research Position in Causal Inference

Harvard University

Cambridge, MA • On-site

$75K/yr

Full-time

Posted 26 days ago

Harvard University rating

8.1

Company rating: 8.1 out of 10

Based on 7 frontline employees who took The Breakroom Quiz

131st of 534 rated colleges and universities

Job description

Position
Details
Title
Postdoctoral Research Position in Causal Inference
School
Harvard T.H. Chan School of Public Health
Department/Area
Biostatistics
Position Description
We invite applications for a full-time Postdoctoral Research Fellow to join the causal inference team supervised by Professor Francesca Dominici. The position will focus on developing and applying novel causal inference methods for large-scale observational studies, with a particular emphasis on environmental exposures and public health. Core data resources include nationwide claims, linked with rich contextual information such as census data, weather records, and high-resolution air pollution and related environmental exposures data.
Motivated by relevant public health and policy questions, the goal is to develop methodologies for the identification, estimation, transportability, and generalization of the causal effects in complex real-world settings. Among others, methodological areas will span:
• Causal inference for spatiotemporal data,
• Methods for heterogeneous treatment effects estimation,
• Methods for multiple exposures, multiple outcomes,
• ML and AI methods for causal inference,
• Bayesian causal inference,
• methods for transportability and generalizability of causal effects across space, time, and populations.
Duties and Responsibilities
• Design, develop and implement novel causal inference methods in the areas listed in the position description.
• Work with large, high-dimensional datasets.
• Lead and contribute to manuscripts for high-impact journals (e.g., top Statistics journals and Nature-like journals).
• Present findings in internal meetings and at national/international conferences.
• Collaborate with an interdisciplinary team (bio)statisticians, data scientists, computer scientists, and climate scientists.
• Contribute to open-source code and reproducible pipelines.
Basic Qualifications
• PhD (completed or near completion) in Statistics, Biostatistics, Data Science, Computer Science or a closely related field.
• Demonstrated expertise in causal inference, with interest in methods development.
• Experience with statistical and ML methods, including at least one of the following: Bayesian methods, deep learning, spatiotemporal modeling, high-dimensional statistics.
• Proficiency in statistical programming (R and/or Python) and good practices for reproducible research.
• Experience working with large datasets and cloud computing environments.
• Excellent written and oral communication skills, with a track record of peer-reviewed publications commensurate with career stage.
• Ability to work in a collaborative, interdisciplinary environment.
Additional Qualifications
Prior experience with one or more of:
• Health claims data, EHRs, or other large-scale health/administrative datasets.
• Environmental, climate, or air pollution exposure data.
Familiarity with LLMs.
Special Instructions
Please submit the following materials:
• Cover letter describing your research interests, relevant experience, and fit for this position.
• Curriculum vitae including a list of publications.
• One to three representative publications or preprints.
Names and contact information for 2-3 references.
Contact Information
Catherine Adcock
Contact Email
catherine_adcock@harvard.edu
Salary Range
$75,000
Minimum Number of References Required
2
Maximum Number of References Allowed
3
Keywords
Causal inference; spatiotemporal modeling; generalizability; transportability; environmental health

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