We are looking for a motivated Ph.D. candidate who wants to develop exciting visual analytics methods to advance the concept of Prescriptive Visual Analytics (PsVA), a step further the idea of predictive visual analytics.

Visual analytics techniques play a central role in predictive analysis by incorporating human intelligence to improve classification model accuracy and evaluate produced outcomes. With a reliable classifier that supports interpretation, the under-explored space of Prescriptive Visual Analytics (PsVA) can be promoted. Different from predictive analytics, which aims to answer the questions "What will happen?" and "Why will it happen?", PsVA focuses on transforming predictions insights into actionable recommendations to answer the questions "What should be done?" and "Why should it be done?". The core of PsVA involves different concepts, ranging from interpretable prediction models to optimization approaches to suggest the best actions based on a prediction and illustrate each action's implications into the predicted outcome.

In this project, the candidate will work to develop new PsVA solutions that support the investigation and understanding of a prediction and allow for the representation of multiple actionable choices and their implications. The candidate will work on cutting-edge topics such as model interpretability & explainability, explainable machine learning visualization, and predictive visual analytics, helping to reduce existing barriers to end-users in taking full advantage of predictive and prescriptive analysis while also seeking to increase trust in the analytical process.

The candidate is expected to author high-quality scientific papers and showcase the outputs of this work at international conferences. The position will be with the Visualization cluster of the TU Eindhoven under the supervision of Dr. Fernando Paulovich. Opportunities for externships with international collaborators are also possible.

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 several techniques that are used on a large scale worldwide.

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

• MSc degree in computer science, computer engineering, or similar.
• Strong programming skills (e.g., Python, Javascript, etc.).
• Good communication skills and excellent command of English (Dutch is not required, although willingness to learn is highly appreciated).
• Capability and willingness to work independently and in an interdisciplinary team.
• Self-motivated, enthusiastic, proactive, goal-oriented, and persistent.
• A solid background in visualization, visual analytics, machine learning, and explainable AI, with strong mathematical affinity.
• Publications in top-tier conferences and journals of visualization will be considered additional advantages.

• A meaningful job in a dynamic and ambitious university with the possibility to present your work at international conferences.
• A full-time employment for four years, with an intermediate evaluation (go/no-go) after nine
• months.
• To develop your teaching skills, you will spend 10% of your employment on teaching tasks.
• High-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At TU/e we challenge you to take charge of your own learning process.
• A gross monthly salary and benefits (such as a pension scheme, pregnancy and maternity leave, and partially paid parental leave) in accordance with the Collective Labor Agreement for Dutch Universities. The salary will be € 2,541 in the first year and € 3,247 in the fourth year. 
• Additionally, an annual holiday allowance of 8% of the yearly salary, plus a year-end allowance of 8.3% of the annual salary.
• Should you come from abroad and comply with certain conditions, you can use the so-called '30% facility', which permits you not to pay tax on 30% of your salary.
• A broad package of fringe benefits, including an excellent technical infrastructure, moving expenses, and savings schemes.
• Family-friendly initiatives are in place, such as an international spouse program and excellent on-campus children's daycare and sports facilities.