The Luxembourg Institute of Science and Technology (LIST) in partnership with a strategic industrial partner will drive significant research and development focused on responding to innovation challenges in tire-
related applications and business intelligence.
The primary objective of this project is to foster research excellence and the development of technologies, skills and tools to capture the value of data generated along the manufacturing and business levels of an industrial partner and use them as innovation asset in the tire-
related application and services. This will be achieved by broad research approaches which will be built on public-private synergy between LIST and our partner.
The project is structured in two programs.
The first program is dedicated to the optimisation of virtual experiments during the design and development of tires manufacturing.
The second program is dedicated to data analytics of other business areas such as marketing, supply chain, etc.
All projects and positions will be co-supervised by LIST senior researchers and our industrial partner professionals who all have a large experience in supervision and project management.
For one of the projects, we are looking for a highly motivated researcher that holds a PhD degree to work on the definition of a framework that allows re-
configuring and selecting between different machine learning models. The work will also look at model parameters tuning, error analysis and quality indicators for model selection.
The framework will be used to optimize a number of predictive services.
PhD degree in quantitative domains such as Computer Science, Statistics, Mathematics with specialization in Machine Learning, Data Analytics and related fields.
Ideally, 2-3 years of relevant research / work experience and demonstrated competencies in the application of advanced analytics and machine learning techniques;
Strong skills in Python and / or R;
Knowledge of classical machine learning techniques is needed;
Experience with recalibration of machine learning models is highly desirable;
Knowledge of optimization techniques is a plus;
Experience with data visualisation tools / libraries is a plus;
Strong interest in engaging in innovative value-creation activities with industrial partners;
Knowledge of the manufacturing domain and the digitalization of the processes is also a plus;
Track record of research dissemination including peer-reviewed publications;
Demonstrated ability to generate new ideas, concepts, models and solutions;
Collaborative skills, initiative, result oriented, organization, and capacity to work in an interdisciplinary environment;
Excellent analytical, and report writing skills;
Excellent communication skills (oral, written, presentation).
Proficient written and spoken English is mandatory.