PhD Scholarship Scientific Computing with Sdg at Eindhoven University of Technology

Eindhoven University of Technology is pleased to invite applicants to apply for a new opening PhD position in computer science. This position is open until filled.

Two 4-year Marie Curie PhD positions are available at TU Eindhoven, starting date March 1, 2019. Research topics may include numerical linear algebra, big data, large-scale optimization, modeling, model reduction, probability and statistics.

This is a description of Position 2 at TU Eindhoven, joint with SDG Milan, This position is "ESR6" (Early-stage researcher), part of the EU Marie Curie EID (European Industrial Doctorate) project BIGMATH, , including 7 PhD positions in total, at universities in Milan, Novi Sad, Lisbon, and Eindhoven.

You will be a member of the Centre for Analysis, Scientific Computing and Applications (CASA), within the Department of Mathematics and Computer Science at TU Eindhoven.

Please find below a brief description from the BIGMATH proposal. Here "RO" stands for research objective. This position is ESR6 (ESR = Early-stage researcher). Project 6 (ESR6): Prediction of failure events in complex productive systems (TU/e & SDG) With the advent of Industry 4. 0, many current industrial processes are subject to continuous monitoring of their efficiency by sensors, which provide numerical data of various types, with a high frequency. The data gathered by these systems have various uses. Two important but difficult objectives are (1) prediction of events that can impair (damage) the process output, and (2) prediction and optimization of the quality of the process end-product. However, the availability of vast amount of data leads business users to even more ambitious objectives: (3) understanding the causes of failures and varying quality, and (4) taking actions to improve the process, to avoid failures or to reduce the failure rates. These goals are very challenging. Good and reliable prediction usually requires both linear and nonlinear models and algorithms, which are often difficult to interpret, so that causal relationships remain unclear. On the contrary, interpretable models often are unable to represent complex, nonlinear and dynamic relationships. Therefore, ESR6 will develop efficient models that are able to predict with a good reliability the occurrence of process impairment, and to tell which features mostly contribute to the damage in the given conditions. Three key mathematical ingredients that we will use are: (a) compositionality: building complex models from simpler components; (b) model reduction: simplification of the model, so that it can be simulated and the parameters identified, without losing much accuracy (RO3); © causality: understanding what is causing what and take improvement-oriented actions (RO4). Current trends for impairment prediction frequently use deep learning models, which are flexible and often provide good results, but usually very hard to interpret. In this project, ESR6 will introduce innovative more interpretable models. Such models will support the development of innovative methods for process optimization and control by ESR7. The relevant research objectives: RO3: Develop model reduction or feature selection techniques for the construction of fit-for-purpose models, which may reduce the complexity of a system, increasing the interpretability of cause-effect relationships. RO4: Develop interpretable statistical models for classification in imbalanced classes and for the prediction of rare events (i. e. classification into 2 imbalanced classes). The aim is to overcome the application of "black box" machine learning techniques, using models that can interpret the interrelationships and the causal effects among different features. Some information on SDG SDG is a global consulting firm with a broad focus on data analytics in all its aspects. The data science practice is firmly rooted in strong mathematical and statistical know-how and a sound computing expertise. SDG is constantly growing by hiring many young people coming directly from universities, and some more experienced people to support the growth in a consistent way.

March 2019: TU Eindhoven (with 1 week course in Milan); AprilAugust 2019: SDG Milan; September 2019August 2020: TU Eindhoven; SeptemberOctober 2020: University of Milan; November 2020 November 2021: SDG Milan; December 2021February 2023: TU Eindhoven.

For informal inquiries, please contact Dr. Michiel Hochstenbach, TU Eindhoven by email. To apply, please use the TU Eindhoven system. by using the “apply now” button.Please include all of this: motivation letter, CV (math interests, languages, some personal info, hobbies), list of BSc and MSc courses with grades, MSc thesis (or draft), list of ca 3 people for recommendation. See also Scanning of the applications will start immediately; closing date December 5, 2018.

You are also very welcome to obtain informal information about the project and SDG via Maurizio Sanarico,

For further information about employment conditions you may contactMarjolein von Reth.HR advisor TU Eindhoven,


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