Doctoral Programme in Computational Sciences

The Doctoral Programme in Computational Sciences is part of the Doctoral School in Science and Engineering (DSSE)

Data and Computational Sciences are multidisciplinary fields based on the idea that Mathematics and Computer Science theories are used to address application domains within Engineering, the Natural Sciences and the Social Sciences. Obtaining meaningful computational results for various research areas demands both domain knowledge and a command of mathematical and computer modelling tools and their implementation, verification, validation and uncertainty quantification. These problems are typically (though not necessarily) solved using the latest generation of high-performance computing (HPC) platforms. In addition, DCS also includes novel approaches for data acquisition, archiving, management (including meta data) and subsequent processing of data.

Mission 

Provide structured doctoral education in the field of Data and Computational Sciences aimed at advancing both fundamental knowledge and its application in challenging domain areas.

Philosophy

Our philosophy is to leverage the mathematical commonality between problems in a variety of multi-disciplinary areas. The doctoral candidates will be supervised by internationally known scientists across Luxembourg’s institutes of research and university. They will be exposed to a mix of application areas where they will gain domain-specific knowledge. At the same time, they will develop a set of computational skills and data science skills that are applicable in a variety of fields.

Key features

• Integrative approach outside of the constraints of traditional subject barriers.
• High-quality supervision by internationally recognised Professors, Group Leaders and Research Scientists.
• A varied range of training courses that can be tailored to the needs of the doctoral candidate.
• Situated within the broader structure of the Doctoral School in Science and Engineering (link through).
• Access to state of the art high-performance computing facilities.
• Annual retreat of two days including team building and poster session.
• Strong links with local industry allowing for secondments.
• Possibility of personal professional mentoring.
• Compulsory training on Ethics in Data and Artificial Intelligence. 
• Dedicated to Open Data, Open Science and Open Source.

Clusters

Core

• Applied Mathematics
• Computer Science
• Data Science and Statistics

Domain areas

• Computational Sciences 
• Geosciences
• Engineering Sciences
• Life Sciences
• Social Sciences