Intern

Location: Amsterdam

, Netherlands

Worker Type

Interns (Fixed Term) (Trainee)

Posting Start Date

October 21, 2026

Job Description

Intern assignment

Describe the responsibilities and activities related to the Assignment. If relevant, relate these activities to the requirements for completion of the Internship as set by the Institute of Education. Include details of department where the Internship will be carried out.

6-months MSc-level internship at Energy Transition Campus Amsterdam, Hydroprocesses Technology Team

Project Title: Enhanced Interpretation of GC×GC Measurements Using Density Functional Theory Density Functional Theory (DFT)

Goals and objectives

Comprehensive two-dimensional chromatography (GC×GC) separates hydrocarbons according to molecular size in the first column and polarity in the second column, resulting in a two-dimensional compositional map. For GTL products, Shell has developed specialized classification methods that assign product yields to hydrocarbon families based on carbon number and degree of branching.

At higher carbon numbers, however, molecules with different branching patterns increasingly overlap and can no longer be distinguished reliably. Furthermore, the current classification provides little information on branch location. Such structural information is important for predicting product roperties such as cloud point, pour point, and viscosity, and may provide valuable insights for catalyst development. This project will investigate whether Density Functional Theory (DFT) calculations can support the interpretation of GC×GC data and improve hydrocarbon classification. Specific objectives are:

Refine the current classification methodology by distinguishing molecular classes that currently overlap, such as tetra- and penta-branched paraffins or ethyl-methyl substituted paraffins.

Assess whether highly branched species (e.g., penta-, hexa-, and hepta-branched paraffins) can be identified more reliably, particularly in highly dewaxed products.

Explore whether branch-position distributions can be inferred for lower carbon numbers and whether suitable descriptors can be developed to differentiate catalyst performance and selectivity.

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