Jobs / Summary

Internship / Thesis - Process Simulation in Chemical Engineering

Confidential company · Berlin · Posted May 27, 2026

Public summary

An internship opportunity at a leading green chemicals company developing sustainable chemical processes from lab to pilot scale. The role involves building and running process simulations, supporting technical assessments, and collaborating with engineering and laboratory teams to impact real-world sustainable chemical technologies. The position is suitable for Master’s students in chemical or process engineering with simulation and Python skills.

Location and work setup

Location
Berlin
Remote status
On-site
German requirement signal
No German Required Detected
Detected job language
English

Responsibilities

Build and run steady-state and dynamic process simulations; integrate and adapt kinetic sub-models within existing simulation environments; support early-stage technical assessments of process concepts; assist with CAPEX and OPEX estimations using templates; document assumptions, calculations, and results clearly; develop programming tools or scripts for data handling and analysis; collaborate closely with engineering and lab teams to ensure results inform practical engineering decisions.

Qualifications

Enrolled in a Master’s program in chemical engineering, process engineering, or a related field; mandatory internship as per study regulations; strong fundamentals in thermodynamics, reaction engineering/kinetics, and separations; hands-on experience with process simulation software such as Aspen Plus, HYSYS, or gPROMS; proficiency in Python including NumPy and Pandas; good analytical mindset and structured documentation skills; ownership of assigned tasks. Experience with dynamic simulation, model simplification, pilot-scale or scale-up topics, and advanced AI or machine learning skills are advantageous.

Skills

process simulation steady-state simulation dynamic simulation kinetic sub-model integration CAPEX/OPEX estimation Python programming NumPy Pandas data analysis technical documentation machine learning (nice-to-have)