Student Intern Pharma Molecular Modeling

Student Intern – Pharma Molecular Modeling

Strategic Student Program (SSP) Netherlands 2026

Siemens Digital Industries Software – #Transform The Everyday

Meet the interns by clicking here!

Location: The Hague, the Netherlands

Working model: On-location / Hybrid

Way of working: Working by goals

Internship duration: 5 months

Siemens Digital Industries Software is a leading provider of solutions for the design, simulation, and manufacture of products across many industries. From advanced materials and polymers to complex engineered systems, our software enables customers to innovate faster and more sustainably. We offer interns a role with real responsibility, autonomy, and the opportunity to contribute meaningfully within a collaborative, international team.

The Strategic Student Program is Siemens Software's global internship program, managed by University Relations. As an SSP intern, you will gain meaningful, real‑world work experience while developing professional and technical skills. Interns benefit from structured learning opportunities, performance evaluations, professional development workshops, access to the Siemens Learning Hub, and participation in a global student community and program events.

The Simcenter Culgi team develops advanced computational modeling solutions that support innovation in molecular sciences across industries, including pharmaceuticals, biotechnology, specialty chemicals, and advanced materials. By combining molecular modeling, simulation, and scientific computing, the team enables customers to better understand molecular behaviour, accelerate research, and make more informed development decisions.

As pharmaceutical research increasingly relies on computational approaches to complement experimental work, molecular modeling plays an essential role in understanding molecular interactions, predicting molecular behaviour, and supporting the design and optimization of new therapeutic compounds and formulations. The team continuously explores new methodologies and workflows that improve simulation efficiency, scientific insight, and data‑driven decision‑making.

During this Strategic Student Program (SSP) internship, you will contribute to ongoing research and development activities within the Pharma Molecular Modeling team. Working alongside experienced scientists and software developers, you will support computational studies involving molecular simulations, data generation, analysis, and interpretation of simulation results.

Depending on the project focus and your academic background, you may contribute to molecular modeling workflows, coarse‑grained simulations, computational chemistry studies, or machine learning‑assisted approaches for molecular systems. You will assist with evaluating simulation methodologies, generating and analysing scientific data, and supporting research activities aimed at improving existing modeling workflows and accelerating scientific discovery.

This internship offers an excellent opportunity to apply academic knowledge to real‑world industrial research while developing practical experience in computational chemistry and pharmaceutical molecular modeling. The project may also be aligned with a bachelor's or master's thesis, subject to university requirements and project scope.

We are looking for master's students in Computational Chemistry, Pharmaceutical Sciences, Chemical Engineering, Physics, Materials Science, Applied Mathematics, Bioinformatics, or a closely related field.

• Strong background in computational chemistry and molecular simulations.
• Solid understanding of machine learning methods; experience with neural networks or graph‑based models is preferred.
• Interest in applying artificial intelligence techniques to scientific and engineering challenges.
• Programming experience in Python.
• Familiarity with coarse‑grained modeling, molecular dynamics, quantum chemistry, or molecular modeling for pharmaceutical applications is an advantage.
• Strong analytical mindset, curiosity, and problem‑solving skills.
• Good…