At Centiro we are constantly on a journey of discovery. We think outside the box and strive to stretch the boundaries of possibilities. We create something unique everyday – something which is more than a software company. We believe in innovation and the strong power of collaboration. Our ambition is to deliver the best solutions for our customers and at the same time be the best possible place to work. Do you want to be a part of our continued journey?

Background

As sustainability reporting becomes an increasingly important part of global supply chains, companies need access not only to emissions data, but also to an understanding of how reliable that data is. Modern logistics platforms calculate CO₂e emissions for shipments using a mix of measured operational data, carrier-specific models and industry-average default values.

While customers receive emissions results, they often have limited insight into the quality and reliability of the underlying data. Different data sources, calculation methods and transport scenarios can significantly affect confidence in the reported emissions. This thesis explores how the quality and reliability of logistics emissions calculations can be assessed, quantified and communicated in a transparent and actionable way.

What are we hoping to achieve?

Design and evaluate a methodology for measuring the quality and reliability of shipment-level emissions calculations, enabling customers to better understand how trustworthy their emissions data is and identify which data improvements would have the greatest impact.

How?

The objective is to investigate and prototype a framework for assessing the quality and reliability of logistics emissions calculations. Areas of interest include:

• Data quality assessment and uncertainty modelling

• Sensitivity analysis and parameter importance

• Dependencies between calculation inputs and calculation methods

• Validation approaches when ground truth data is limited or unavailable

• Visualization and communication of data quality to support customer decision-making

The work may draw on methods from statistics, uncertainty quantification, life cycle assessment, operations research and data science. The student is encouraged to propose their own approach.

Skills:

Programming experience is required, preferably in Python or a similar language for data analysis and scientific computing. Familiarity with statistics, data analysis, modelling, simulation or machine learning is beneficial. Experience with tools such as Jupyter Notebooks, Pandas, NumPy, and SciPy is a merit.

When:

Spring 2027

You are…

A student studying Applied Mathematics, Engineering Mathematics, Statistics, Data Science, Industrial Engineering, Sustainability Science or a related field.

You have experience with programming and a solid foundation in mathematics, statistics or data analysis. Familiarity with data modelling, sensitivity analysis or computational science is beneficial. You are comfortable working with data-driven problems and interested in understanding how the quality and reliability of emissions calculations can be assessed and communicated.

An interest in sustainability, logistics and data driven decision making is a plus.