Shell

Intern

AmsterdamPosted Nov 7, 2026

Requirements at a glance

Intern at Shell is a role based in Amsterdam. It does not mention visa sponsorship. Key skills include Machine learning. Applications are submitted through Workday.

Visa sponsorship
Not mentioned in the posting
Application platform
Workday

Skills in this posting

  • Machine learning

Extracted automatically from the job description. Confirm details in the full posting below.


, Netherlands


Worker Type:

Interns (Fixed Term) (Trainee)


Posting Start Date:

November 7, 2026


Job Description:

DESCRIPTION OF INTERN ASSIGNMENT


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.

 

<INSERT DESCRIPTION RESPONSIBILITIES/ACTIVITIES ASSIGNMENT>

 

6-months MSC project internship based at ETCA

 

Title : Towards the Next Generation of Hybrid Second Order Group Contribution Frameworks for the Prediction of the Melting Point and Viscosity of Heavy and Branched Alkanes

 

Goals and objectives

 

Accurate prediction of melting point and viscosity remains challenging for heavy and highly branched alkanes due to the strong influence of molecular architecture, conformational flexibility, and intermolecular interactions. To address this need, a next-generation of Group Contribution framework for predicting the melting point and viscosity of heavy and branched alkanes may be formulated by augmenting classical first- and second-order structural groups with DFT-derived molecular descriptors and machine-learning corrections. The GC component captures the contribution of molecular fragments and branching patterns, while DFT quantifies electronic and conformational features governing intermolecular forces, crystal lattice stability, and liquid flow behavior. Machine learning algorithms are then used to learn higher-order interactions between structural groups and quantum-mechanical descriptors, providing correction terms to the classical GC predictions. Such a DFT-ML-GC hybrid methodology retains the simplicity, interpretability, transferability and extrapolation capability of second order group contribution approaches while significantly enhancing prediction accuracy for complex hydrocarbon systems beyond the range of available experimental measurements.

 

Deliverable:

MSc thesis and final presentation

 

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