2 PhD Positions on the ITEA project Inno4Health

Two PhD Positions Systemic Change Group, Department of Industrial Design, Data Mining Group, Department of Mathematics and Computer Science.

The department of Industrial Design is one of the nine departments of the Eindhoven University of Technology (TU/e). Research at the department is focused on two areas or thematic clusters: Systemic Change and Future Everyday.

The Systemic Change cluster focuses on designing innovations that have impact on systemic structures and groups of people, ultimately aiming to address large-scale issues such as urban health, future mobility and sustainability. Field data is used in novel iterative and circular research-through-design processes involving strategic alliances of stakeholders.

'Systemic Change uses Design and Technology to study socio-technical systems at the level of a community, by designing interventions addressing societal challenges and analyzing their effect on the eco-system'. Systemic change is the extension to standard Human-Computer Interaction Processes and Methods to address societal change.

The Systemic Change cluster focuses on the co-creation of socio-technical systems operating in semi-open real-life ecosystems or field labs, with the aim to address clearly defined societal challenges and study the nature, drivers and opportunities for sustainable change induced by these systems on an ecosystem level. Main focus is the usage of emerging technology (e.g. sensors and actuators, visualization of data, application of AI methods or datamining crowd platforms) to envision, design and evaluate these systems and their longitudinal effects. To enable these long-term studies, we develop the necessary methods, tools, platforms, spaces and field labs to support the co-creation and analyses of these socio-technological systems in cross-disciplinary stakeholder teams.

This project is carried out in close collaboration with the department of Mathematics and Computer Science. In the department Computer Science the section Information Systems studies subjects related to the design, realization, analysis and optimization of information systems. In this section the Data Mining (DM) chair studies techniques and knowledge discovery approaches that are at the core of data science. The group is known for its contributions to the areas of predictive analytics, automation of machine learning and networked science, subgroup discovery and exceptional model mining, and similarity computations on complex data. In this group one research line, lead by dr. Joaquin Vanschoren, concentrates on the progressive automation of machine learning. Dr. Vanschoren founded and leads OpenML.org, an open science platform allowing scientists to share datasets and train many machine learning models from many software tools in a frictionless yet principled way. It also organizes all results online, providing detailed insight into the performance of machine learning techniques, and allowing a more scientific, data-driven approach to building new machine learning systems. Subsequently, he uses this knowledge to create automatic machine learning (AutoML) techniques that learn from these experiments to help people build better models, faster, or automate the process entirely. Machine learning is the science of making computers act without being explicitly programmed. Instead, algorithms are used to find patterns in data.