We are looking for a motivated PhD candidate that wants to develop new methods on the cross-border between Visual Analytics (VA), Machine Learning (ML) and explainable AI. The candidate will be developing new VA methods and strategies for making sense and comparing image-to-image ML models.

Position: PhD-student
Department(s): Department of Mathematics & Computer Science
FTE: 1,0
Date off: 31/07/2020
Reference number: V32.4440

Project description

The development of reliable Machine Learning algorithms for solving image-to-image problems like in medical image acquisition, requires a paradigm shift. On the algorithm development itself, there is a shift from theory-based models to data-based models or a combination of those. The new algorithms/models (often deep neural networks) outperform traditional methods, but they are notoriously inefficient to develop due to the trial-and-error nature of model development. Furthermore, they are also hard to trust and interpret due to the lack of explanation of the results. Visual Analytics solutions have shown potential for the explanation of ML models and their performance. The PhD in this vacancy will propose new visual analytics methods for ML models that are used in the context of medical imaging acquisition. In collaboration with ML researchers, the PhD will aim at providing the research community, industry, and clinical end users with visual analysis strategies to analyze, interpret, and improve their ML models and training data.

The project will be developed within the visualization cluster under the supervision of Prof.dr. Anna Vilanova and Dr. Nicola Pezzotti (Philips- part time TU/e) and in strong collaboration with experts in Machine Learning for acquisition purposes from the Electrical Engineering department at TU/e Dr.ir. Ruud van Sloun (TU/e – part time Philips).

The visualization cluster (https://research.tue.nl/en/organisations/visualization) at TU/e has a strong track record in visualization and visual analytics for ML models and high-dimensional data. It has generated several award winning contributions at major visualization conferences (IEEE VIS, IEEE InfoVis, IEEE VAST, EuroVis); several successful start-up companies (MagnaView, Process Gold and SynerScope); and a number of techniques that are used on a large scale world-wide.

The visualization cluster participates actively in the newly created Eindhoven AI System Institute (EAISI) and Data Science initiatives at TU/e.

Job requirements

We are looking for a candidate who meets the following requirements:

• You are enthusiastic about research in visual analytics and machine learning;
• You have experience with or a strong background in visualization, visual analytics and/or machine learning. Preferably you finished a master in Computer Science, (Applied) Mathematics, (Applied) Physics or Electrical Engineering;
• Expertise in the fields of explainable AI is a plus but not mandatory;
• You have strong programming skills;
• You have good communication skills and are able to work in a team;
• You are creative, ambitious, hardworking and persistent;
• You have a good command of the English language (knowledge of Dutch is not required).

Conditions of employment

We offer you:

• A fixed-term, full-time contract for four years at Eindhoven University of Technology
• A salary is in accordance with the Collective Labour Agreement of the Dutch Universities, increasing from € 2.325 per month initially, to € 2.972 in the fourth year.
• An attractive package of fringe benefits, including end-of-year bonus (8,3% in December), an extra holiday allowance (8% in May), moving expenses and excellent sports facilities.

Information and application

For more information about the project please contact Prof.dr. Anna Vilanova (a.vilanova[at]tue.nl).

More information on employment conditions can be found here: https://www.tue.nl/en/working-at-tue/why-tue/compensation-and-benefits/

You can apply by using the ‘Apply now’ button on top of this page. Applications should include:

• an application letter
• a detailed CV (including a list of publications if available)
• a list of courses taken and grades obtained
• a copy or a link to your Master’s thesis

You can upload all the required documents in max. five pdf’s (max. 10 Mb).

Please click here to learn more.