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Internship Computational Materials Science Jobs (NOW HIRING)

D.) in Materials Science, Mechanical Engineering, or a related field, with more than ten years of experience in computational modeling. * Expertise in metallurgy and materials science, as well as ...

D.) in Materials Science, Mechanical Engineering, or a related field, with more than ten years of experience in computational modeling. * Expertise in metallurgy and materials science, as well as ...

D.) in Materials Science, Mechanical Engineering, or a related field, with more than ten years of experience in computational modeling. * Expertise in metallurgy and materials science, as well as ...

Post-Doctoral Fellow

Worcester, MA ยท On-site

$45K - $70K/yr

... computational materials science with a focus on advanced density functional theory (DFT), machine learning, and multiscale materials modeling for alloys and ceramics. The successful candidate will ...

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Internship Computational Materials Science information

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How much do internship computational materials science jobs pay per hour?

As of Jul 3, 2026, the average hourly pay for internship computational materials science in the United States is $19.31, according to ZipRecruiter salary data. Most workers in this role earn between $16.11 and $20.91 per hour, depending on experience, location, and employer.

What is an internship in computational materials science?

An internship in computational materials science is a temporary position, usually for students or recent graduates, where you gain hands-on experience using computer simulations and modeling to study and design materials. Interns typically work on research projects involving software tools to predict material properties, analyze data, or develop new materials. This experience provides valuable skills in programming, data analysis, and scientific research, which are essential for a career in materials science or related fields.

What is the difference between Internship Computational Materials Science vs Computational Materials Scientist?

AspectInternship Computational Materials ScienceComputational Materials Scientist
CredentialsEnrolled in or recent graduate of relevant degree programsAdvanced degree (Master's or Ph.D.) in materials science, physics, or related fields
Work EnvironmentAcademic or research labs, internships at industry companiesResearch and development teams in industry or academia
ResponsibilitiesAssisting with computational modeling, data analysis, learning industry toolsLeading projects, developing models, publishing research

Internship Computational Materials Science positions are entry-level, focused on learning and supporting ongoing projects, while Computational Materials Scientists are experienced professionals responsible for independent research and development in the field.

What types of projects can an intern expect to work on in a Computational Materials Science internship?

As a Computational Materials Science intern, you can expect to be involved in projects such as simulating material properties, analyzing large datasets from experiments, or developing and testing computational models. Interns often assist with software coding, data visualization, and running simulations using tools like Density Functional Theory (DFT) or molecular dynamics packages. Collaboration with other scientists and engineers is common, and you may contribute to research publications or presentations, providing valuable hands-on experience in both individual and team-based research settings.

What are the key skills and qualifications needed to thrive as an Internship Computational Materials Science, and why are they important?

To thrive as an intern in Computational Materials Science, you typically need a solid background in materials science, physics, or related fields, along with programming skills in languages such as Python or C++. Familiarity with computational tools like Density Functional Theory (DFT) software (e.g., VASP, Quantum ESPRESSO) and data analysis platforms is highly beneficial. Strong analytical thinking, attention to detail, and effective communication set candidates apart in collaborative research environments. These competencies enable interns to contribute meaningfully to simulations, data interpretation, and innovative materials research.
More about Internship Computational Materials Science jobs
What cities are hiring for Internship Computational Materials Science jobs? Cities with the most Internship Computational Materials Science job openings:
What are the most commonly searched types of Computational Materials Science jobs? The most popular types of Computational Materials Science jobs are:
What states have the most Internship Computational Materials Science jobs? States with the most job openings for Internship Computational Materials Science jobs include:
Infographic showing various Internship Computational Materials Science job openings in the United States as of June 2026, with employment types broken down into 1% As Needed, 98% Full Time, and 1% Part Time. Highlights an 90% Physical, 1% Hybrid, and 9% Remote job distribution, with an average salary of $40,174 per year, or $19.3 per hour.
P2 Science - Principal Scientist, AI/ML Discovery Platforms

P2 Science - Principal Scientist, AI/ML Discovery Platforms

Connecticut Innovations

Woodbridge, CT โ€ข On-site

Other

Posted 2 days ago


Job description

Are you ready to join Connecticut Innovationโ€™s vibrant community of innovators? Connecticut Innovations (โ€œCIโ€) is Connecticutโ€™s strategic venture capital arm, and we are passionate about serving our portfolio of 220+ companies across various industries, with strengths in life sciences, technology, and climate tech.


Come join P2 Science, Home - P2 Science Inc. | The New Green Chemistry Company


About P2 Science

P2 Science is a green chemistry company that develops high-performance specialty ingredients from renewable feed stocks using its proprietary PICEยฎ process. P2โ€™s ingredients serve the personal care and fragrance markets, delivering performance and sustainability to global customers.


Principal Scientist, AI/ML Discovery Platforms

The Principal Scientist, AI/ML Discovery Platforms, serves as the technical point person for P2 Scienceโ€™s AI/ML-enabled catalyst and materials discovery programs. This individual leads the deployment and ongoing operation of P2โ€™s Self-Driving Laboratory (SDL) and its associated central operating system, integrating high-throughput experimentation, AI/ML-driven optimization, and multi-scale catalyst-reactor modeling into a single closed-loop discovery engine. The role partners with internal chemists, engineers, and external program sponsors to advance commercially relevant catalyst and process innovations from concept through pilot validation, and reports directly to the President / Chief Scientific Officer.


Responsibilities / Job Functions:

  • Lead the deployment, integration, and ongoing operation of P2โ€™s Self-Driving Laboratory (SDL), including orchestration software, central operating system, robotic dosing and sample handling, automated analytical instruments, and provenance-tracking databases.
  • Serve as the primary technical lead for one or more AI/ML-driven catalyst and materials discovery programs, owning workflow architecture, experimental design, model selection, and closed-loop optimization strategy.
  • Develop, maintain, and validate computational models โ€” including Bayesian optimization, multi-fidelity surrogate modeling, descriptor-based clustering, and physics-informed multi-scale catalyst-reactor models.
  • Design and execute high-throughput proxy screening campaigns (e.g., differential scanning calorimetry, FTIR, GC-MS / GC-FID) and translate proxy signals into validated reaction performance metrics.
  • Own the technical reporting cadence with external program sponsors and program directors, including milestone status updates, technical progress reports, go/no-go readiness packages, and program review presentations.
  • Partner with the President / CSO on the overall scientific and commercial direction of AI/ML discovery programs, contributing to program strategy, performance target setting, and prioritization across the project portfolio.
  • Lead intellectual property activities tied to the discovery programs, including freedom-to-operate analyses, invention disclosures, and patent strategy formulation.
  • Mentor and direct the work of junior computational scientists, postdoctoral researchers, and laboratory technicians supporting the SDL and modeling workflows.
  • Collaborate with P2 chemists and process engineers to translate computationally identified catalyst leads into kg-scale technical catalyst syntheses and continuous-flow reactor validation.
  • Maintain rigorous data and model versioning practices, ensuring reproducibility, auditability, and regulatory-readiness across all computational and experimental outputs.
  • Establish and maintain QA/QC procedures for the SDL, including automated exception handling, replicate-variance monitoring, and provenance tracking across instrument, sample, and model events.
  • Identify, evaluate, and integrate new instruments, software tools, and computational methods to expand the capability and throughput of the SDL.
  • Build and maintain external research collaborations with academic, national-laboratory, and industrial partners contributing to AI/ML and SDL workflows at P2.
  • Contribute to scientific publications, conference presentations, and technical white papers that establish P2โ€™s leadership in AI-enabled materials and catalyst discovery.
  • Identify and pursue new external funding opportunities (federal, state, and private) for AI/ML-driven materials and catalyst discovery programs aligned with P2โ€™s commercial strategy.


Education and Experience:

  • Ph.D. in Chemistry, Chemical Engineering, Computational Materials Science, or a closely related discipline
  • 2โ€“3 years of relevant post-doctoral, industry, or national-laboratory experience applying AI/ML, Bayesian optimization, or computational modeling to catalyst, reaction, or materials discovery problems
  • Training or research experience in organic chemistry โ€” particularly catalysis, reaction mechanism, or transformations of unsaturated hydrocarbons or biorenewable feedstocks (Highly preferred).
  • Hands-on experience deploying or operating a Self-Driving Laboratory or comparable closed-loop experimental platform.
  • Experience interfacing with external program sponsors, government program managers, or industrial partners on technical reporting and milestone reviews.
  • Track record of peer-reviewed publications and/or patents in computational chemistry, materials informatics, or catalyst discovery.


Required Knowledge and Skills:

  • Strong proficiency in Python (or equivalent) for scientific computing, machine learning, and laboratory automation; familiarity with PyTorch, scikit-learn, or comparable ML frameworks.
  • Hands-on experience with Bayesian optimization, surrogate modeling, descriptor engineering, and active-learning approaches applied to chemistry or materials problems.
  • Working knowledge of laboratory automation, SDL orchestration platforms (e.g., ChemOS, SiLA 2), graph databases (e.g., Neo4j), and integration of automated analytical instruments.
  • Demonstrated ability to translate experimental data โ€” kinetics, spectroscopy, thermal analysis โ€” into mechanistic insight and quantitative kinetic / thermodynamic models.


Traits for Success:

  • Strong written and verbal communication skills, including experience preparing technical progress reports, milestone packages, and external presentations to senior stakeholders and program directors.
  • Self-motivated with the ability to work with minimal supervision in a rapidly evolving research environment.
  • Excellent collaboration skills across multidisciplinary teams of chemists, engineers, and data scientists.
  • Strong technical aptitude and intellectual curiosity, with a record of moving from open-ended scientific questions to validated, decision-ready answers.
  • Ability to manage multiple concurrent projects, deadlines, and external reporting obligations in parallel.
  • Trustworthy, high-integrity individual who can work well in a team environment and represent P2 to external partners and program sponsors


Safety Responsibilities:

  • Wears/uses required PPE at all times when in laboratory or pilot-plant environments, and ensures all SDL operators, visitors, and trainees wear/use required PPE.
  • Follows all safety policies, instructions, and rules in laboratory, pilot-plant, and instrument-room environments; ensures team members and trainees follow the same.
  • Follows all regulations for handling hazardous waste, organic feedstocks, and laboratory chemicals.


Physical Demands

  • Position is primarily a laboratory- and office-based role with occasional time in pilot-plant environments. Must be able to stand and walk for extended periods and occasionally lift up to 25 lbs of laboratory equipment, instruments, and supplies.
  • Must be medically capable of wearing all required laboratory PPE, including respirators when handling specific chemicals, which requires a clean-shaven face for suitable fit under OSHA regulations.


Why Join P2 Science

  • Work with proprietary, differentiated green chemistry
  • High level of ownership and autonomy
  • Direct impact on product development and commercial success
  • Collaborative, fast-moving, and mission-driven team environment
  • Opportunity to shape a technology pipeline from the ground up


Connecticut Innovations and its portfolio companies are equal opportunity employers. All employment decisions are based on qualifications, merit, and business needs, without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, disability, or age.