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Intern Computational Materials Science Jobs in Florida

Materials Science Engineering The Opportunity A postdoctoral position is available in computational and theoretical materials science in the Materials Theory, Informatics, and Design Group, led by ...

The Computational Materials for Energy Technologies (CoMET) group, led by Assistant Professor Duy ... D. in Physics, Chemistry, Materials Science, Chemical Engineering or related fields. Preferred ...

Quality Assurance Intern Department: Quality Employment Type: Internship Location: Gainesville, FL ... Materials Science or equivalent from an accredited institution required Experience: * Must be ...

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Intern Computational Materials Science information

What is the difference between Intern Computational Materials Science vs Intern Materials Engineering?

AspectIntern Computational Materials ScienceIntern Materials Engineering
Required CredentialsUndergraduate or graduate in materials science, physics, or related fields; basic programming skillsUndergraduate or graduate in materials engineering, mechanical engineering, or related fields; foundational technical knowledge
Work EnvironmentResearch labs, computational modeling, data analysisDesign, testing, and development in labs or manufacturing settings
Industry UsageResearch institutions, tech companies, aerospace, academiaManufacturing firms, product development, construction

Intern Computational Materials Science focuses on computational modeling and simulations of materials properties, while Intern Materials Engineering emphasizes practical design, testing, and application of materials. Both roles require a background in materials-related fields but differ in their core activities and work environments.

What does an intern in computational materials science do?

An Intern in Computational Materials Science assists in research and development by applying computational techniques to study and predict the properties and behaviors of materials. Typical tasks include running simulations, analyzing data, and working with software tools to model materials at the atomic or molecular level. Interns may collaborate with researchers to design experiments, interpret results, and contribute to scientific publications or reports. This role provides hands-on experience in both computational methods and materials science, helping to bridge theory and practical application.

What are the key skills and qualifications needed to thrive as an intern in computational materials science, and why are they important?

To thrive as an Intern in Computational Materials Science, you need a solid background in materials science, physics, or engineering, along with coursework in computational modeling and data analysis. Familiarity with programming languages like Python or MATLAB, experience with simulation software (such as VASP or LAMMPS), and knowledge of high-performance computing are typically required. Strong analytical thinking, attention to detail, and effective teamwork are important soft skills for success in collaborative research environments. These skills enable interns to contribute meaningfully to research projects, analyze complex materials data, and communicate findings clearly within multidisciplinary teams.

What types of projects can an intern in computational materials science expect to work on during their internship?

As an Intern in Computational Materials Science, you can expect to engage in projects involving simulations of material properties, data analysis from computational experiments, and the development of models to predict material behavior. You may collaborate with researchers and senior scientists to support ongoing investigations or help optimize simulation workflows. These projects often require proficiency in programming languages such as Python or MATLAB and may involve the use of specialized software like VASP or LAMMPS. The experience provides a hands-on understanding of how computational methods contribute to advancing materials research and often includes opportunities to present your findings to the team.

What are the most commonly searched types of Computational Materials Science jobs in Florida?

The most popular types of Computational Materials Science jobs in Florida are:

What are popular job titles related to Intern Computational Materials Science jobs in Florida?

For Intern Computational Materials Science jobs in Florida, the most frequently searched job titles are:

What cities in Florida are hiring for Intern Computational Materials Science jobs?

Cities in Florida with the most Intern Computational Materials Science job openings:

Senior Materials Expert - AI‑Driven Materials Discovery

Siemens Energy, Inc.

Orlando, FL • On-site

Full-time

Medical, Retirement, PTO

Posted 17 days ago


Siemens Energy rating

8.3

Company rating: 8.3 out of 10

Based on 87 frontline employees who took The Breakroom Quiz

115th of 490 rated machine equipment manufacturers


Job description

A Snapshot of Your Day
As a Senior Materials Expert - AI-Driven Materials Discovery, you will lead the next generation of industrial materials innovation by leveraging artificial intelligence, advanced simulation, and emerging computing technologies to accelerate materials discovery and optimization across Siemens Energy's value chain. You will bridge materials science expertise with digital technologies by developing AI-driven workflows, computational models, and materials data platforms that reduce development time, improve performance, and enable scalable industrial solutions. Working across R&D, Engineering, IT, and business functions, you will lead interdisciplinary teams, drive strategic innovation initiatives, and translate scientific advancements into impactful applications and intellectual property.
How You'll Make an Impact
  • Lead the development and execution of AI-driven materials discovery strategies by designing machine learning models, computational simulations, and digital materials twins to predict material properties and accelerate innovation cycles
  • Develop and integrate advanced computational workflows, including AI/ML models, multi-scale simulations, density functional theory (DFT), molecular dynamics (MD), finite element modeling (FEM), and emerging quantum computing approaches to enable next-generation materials development
  • Build and manage materials data ecosystems by establishing data pipelines, supporting FAIR data standards, curating materials databases, and integrating experimental and computational datasets to improve data-driven decision-making
  • Conduct advanced materials research focused on structure-property-process relationships, including material selection, composites, polymers, alloys, metallurgy, characterization, testing, and failure analysis to support industrial applications
  • Lead interdisciplinary R&D programs by managing technical roadmaps, project milestones, research reviews, external partnerships, and collaboration across global teams, scientific networks, and innovation communities
  • Drive process innovation and knowledge development by mentoring scientists and engineers, establishing new research methodologies, communicating technical findings to leadership, and translating scientific results into scalable business solutions and intellectual property

What You Bring
  • Ph.D. or Master's degree in Materials Science, Chemical Engineering, Physics, Computer Science, Engineering, or a related technical field with a focus on computational materials science or a comparable discipline
  • 8+ years of experience leading research, engineering, or technology development projects in materials science, computational modeling, AI-driven innovation, or industrial R&D environments
  • Deep expertise in computational materials science, materials informatics, machine learning, data science, and simulation methods, with demonstrated experience applying AI/ML techniques such as neural networks, Bayesian optimization, generative models, or graph neural networks (GNNs)
  • Strong experience with materials modeling, experimental-computational integration, HPC environments, large-scale data processing, and advanced simulation techniques; knowledge of quantum computing concepts and algorithms for materials modeling preferred
  • Proven ability to lead cross-functional and matrix teams, influence stakeholders, manage complex technical programs, and translate scientific research into practical industrial applications
  • Excellent analytical, problem-solving, communication, and strategic thinking skills with the ability to collaborate effectively in a global, flexible, and innovation-focused environment; advanced English proficiency required, German language skills beneficial

Who is Siemens Energy?
At Siemens Energy, we are more than just an energy technology company. With ~100,000 dedicated employees in more than 90 countries, we develop the energy systems of the future, ensuring that the growing energy demand of the global community is met reliably and sustainably. The technologies created in our research departments and factories drive the energy transition and provide the base for one sixth of the world's electricity generation.
Our global team is committed to making sustainable, reliable, and affordable energy a reality by pushing the boundaries of what is possible. We uphold a 150-year legacy of innovation that encourages our search for people who will support our focus on decarbonization, new technologies, and energy transformation.
Find out how you can make a difference at Siemens Energy: h ttps://www.siemens-energy.com/employeevideo
Rewards
  • Career growth and development opportunities; supportive work culture
  • Company paid Health and wellness benefits
  • Paid Time Off and paid holidays
  • 401K savings plan with company match
  • Family building benefits
  • Parental leave

https://jobs.siemens-energy.com/jobs
Equal Employment Opportunity Statement
Siemens Energy and Siemens Gamesa Renewable Energy is an Equal Opportunity and Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to their race, color, creed, religion, national origin, citizenship status, ancestry, sex, age, physical or mental disability unrelated to ability, marital status, family responsibilities, pregnancy, genetic information, sexual orientation, gender expression, gender identity, transgender, sex stereotyping, order of protection status, protected veteran or military status, or an unfavorable discharge from military service, and other categories protected by federal, state or local law.
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