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$83.5K

How much do postdoctoral organic synthesis jobs pay per year?

As of Sep 7, 2026, the average yearly pay for postdoctoral organic synthesis 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 is a postdoctoral organic synthesis?

A Postdoctoral Organic Synthesis position is a research-focused role for individuals who have recently completed their PhD in chemistry or a related field, specializing in the design and construction of organic molecules. Postdoctoral researchers in organic synthesis work in academic, government, or industrial laboratories to develop new synthetic methodologies, optimize reactions, and contribute to scientific publications. These positions are typically temporary, lasting one to three years, and serve as a bridge between doctoral studies and permanent research or faculty roles. The work often involves collaboration with other scientists, mentoring graduate students, and presenting research findings at conferences.

What are the key skills and qualifications needed to thrive as a postdoctoral organic synthesis researcher?

To thrive as a Postdoctoral Organic Synthesis researcher, you need a PhD in organic chemistry, advanced knowledge of synthetic methodologies, and experience with multi-step synthesis. Familiarity with analytical instruments such as NMR, HPLC, and mass spectrometry, as well as literature search tools like SciFinder, is typically required. Strong problem-solving skills, attention to detail, and the ability to communicate results clearly are standout soft skills in this role. These competencies are essential for advancing complex research projects, ensuring experimental accuracy, and contributing effectively to scientific teams.

What are some common challenges faced by postdoctoral organic synthesis researchers, and how can they be addressed?

Postdoctoral researchers in organic synthesis often encounter challenges such as optimizing complex multi-step reactions, troubleshooting unexpected results, and managing tight project deadlines. Collaboration with other chemists and regular communication with supervisors can help overcome technical hurdles. Staying organized, keeping detailed lab notebooks, and remaining adaptable to changes in research direction are vital for success. Engaging in lab meetings and networking within the scientific community also helps in sharing knowledge and finding solutions to common problems.

What is the difference between Postdoctoral Organic Synthesis vs Postdoctoral Medicinal Chemistry?

AspectPostdoctoral Organic Synthesis

Postdoctoral Organic Synthesis focuses on designing, developing, and optimizing chemical reactions to create complex organic molecules. It often involves laboratory experimentation, synthesis of new compounds, and publication of research findings. This role typically requires a Ph.D. in Chemistry or a related field, with skills in laboratory techniques and organic reaction mechanisms. The work environment is primarily research labs in academia or industry, and the role is often a stepping stone for academic or industrial careers in chemical research.

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Postdoctoral Organic Synthesis jobs near you
Infographic showing various Postdoctoral Organic Synthesis job openings in the United States as of August 2026, with employment types broken down into 75% Full Time, 22% Part Time, 1% Temporary, 1% Contract, and 1% Nights. Highlights an 84% Physical, 2% Hybrid, and 14% Remote job distribution, with an average salary of $59,022 per year, or $28.4 per hour.

Postdoctoral Appointee - Materials Informatics and Autonomous Synthesis

Argonne National Laboratory

Lemont, IL • On-site

$72K - $121K/yr

Full-time

Re-posted 18 days ago

Job description

The Center for Nanoscale Materials (CNM) at Argonne National Laboratory invites applications for a postdoctoral research position focused on developing AI/ML methods for autonomous materials discovery and synthesis.
We are seeking a creative and collaborative researcher who is excited by the opportunity to help shape the future of autonomous synthesis and self-driving laboratories. This role is ideal for someone who enjoys working at the intersection of data science, machine learning, materials research, and experiment, and who is motivated to translate computational advances into real laboratory workflows.
The position will focus on building the data resources, predictive models, and closed-loop decision frameworks needed to accelerate experimentation and advance next-generation autonomous laboratories. The broader goal is to enable AI-driven materials discovery, autonomous synthesis, and the development of high-quality, reusable datasets that support adaptive experimentation and long-term scientific impact.
This research may include applications in areas such as organic electrochemical and neuromorphic devices, but the central emphasis is on creating data-driven methods and infrastructure that can guide experiments, improve efficiency, and strengthen collaboration between computation and experiment.
Key Responsibilities
  • Develop machine learning-ready data resources for materials by integrating literature, in-house, and newly generated experimental data
  • Build surrogate and predictive models that connect composition, molecular structure, synthesis and processing conditions, morphology, and device-relevant properties
  • Design active learning, Bayesian optimization, uncertainty-aware modeling, and other adaptive experimental design workflows to guide experiments and improve data efficiency in autonomous platforms such as the Polybot
  • Work closely with experimental researchers to integrate AI/ML workflows into closed-loop autonomous synthesis, fabrication, and characterization; translate model predictions into experimental campaigns; and update models using newly acquired data
  • Contribute to strategies for generating diverse, high-value datasets, identifying meaningful descriptors and representations, and building reproducible computational pipelines, workflow automation, and data infrastructure that support long-term autonomous laboratory capabilities
  • Share research outcomes through publications, presentations, software, datasets, and internal reports

Position Requirements
  • Recent or soon-to-be-completed PhD (within the last 0-5 years) in chemistry, chemical engineering, materials science, polymer science, physics, computer science, and/or data science
  • Demonstrated accomplishments in materials informatics, scientific machine learning, or AI-guided experimental design
  • Strong Python and scientific computing skills, including experience with tools such as NumPy, pandas, scikit-learn, and machine learning frameworks such as PyTorch, TensorFlow, or similar
  • Experience developing surrogate models, predictive models, or adaptive learning workflows for scientific or engineering applications
  • Strong interest in working closely with experimental researchers in a laboratory-centered environment
  • Evidence of independent research productivity through publications, software, datasets, or similar outputs
  • Excellent communication skills, the ability to work effectively in interdisciplinary teams
  • Ability to model Argonne's core values of impact, safety, respect, integrity, and teamwork

Preferred Qualifications
  • Experience with active learning, Bayesian optimization, adaptive experimental design, reinforcement learning for experiments, or uncertainty quantification
  • Experience with autonomous, self-driving, or robotic laboratory platforms
  • Background in electronic polymers, conjugated polymers, organic semiconductors, soft materials, electrochemical materials, or related functional materials
  • Experience integrating literature, experimental, and simulation datasets into unified, machine learning-ready workflows
  • Familiarity with cheminformatics or polymer informatics, molecular representations, descriptor engineering, RDKit, characterization-informed modeling, multimodal data fusion, interpretable machine learning, NLP, text mining, or automated extraction of materials data from the literature
  • Experience with workflow automation, data infrastructure, database development, reproducible research pipelines, and collaborative environments that span computation, data science, and experiment

Application Materials
  • Updated CV/Resume
  • Unofficial Ph.D. transcripts
  • If already awarded, a copy of the Ph.D. diploma

Job Family
Postdoctoral
Job Profile
Postdoctoral Appointee
Worker Type
Long-Term (Fixed Term)
Time Type
Full time
The expected hiring range for this position is $72,879.00-$121,465.00.
Please note that the pay range information is a general guideline only. The pay offered to a selected candidate will be determined based on factors such as, but not limited to, the scope and responsibilities of the position, the qualifications of the selected candidate, business considerations, internal equity, and external market pay for comparable jobs. Additionally, comprehensive benefits are part of the total rewards package.
Click here to view Argonne employee benefits!
As an equal employment opportunity employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a safe and welcoming workplace that fosters collaborative scientific discovery and innovation. Argonne encourages everyone to apply for employment. Argonne is committed to nondiscrimination and considers all qualified applicants for employment without regard to any characteristic protected by law.
Argonne employees, and certain guest researchers and contractors, are subject to particular restrictions related to participation in Foreign Government Sponsored or Affiliated Activities, as defined and detailed in United States Department of Energy Order 486.1A. You will be asked to disclose any such participation in the application phase for review by Argonne's Legal Department.
All Argonne offers of employment are contingent upon a background check that includes an assessment of criminal conviction history conducted on an individualized and case-by-case basis. Please be advised that Argonne positions require upon hire (or may require in the future) for the individual be to obtain a government access authorization that involves additional background check requirements. Failure to obtain or maintain such government access authorization could result in the withdrawal of a job offer or future termination of employment.

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