埃因霍温理工大学PhD position in Multi-Faceted Visual Process Analytics申请条件要求-申请方
主办方
埃因霍温理工大学
PhD直招介绍
Introduction
Are you eager to research and develop Visual Analytics techniques for Event Sequence exploration and analysis? Are you curious how to combine and explore the synergy between Visualization and Process Mining? In this project we aim to transform the way we analyze and visualize complex processes.
Job Description
The field of process mining, a branch of business process management, revolves around discovering, analyzing, and optimizing business processes. It does so by extracting insights from event logs, structured records that capture sequences of activities, such as steps in a loan approval workflow, product purchases by customers, or patient journeys across hospitals. However, traditional process mining techniques focus primarily on control-flow, which describes the chain of activities. This singular focus often neglects other dimensions of processes, such as the time between activities and the time required for specific steps, the spatial distribution of tasks across locations, or the intricate relationships between resources and attributes involved in the processes. For example, in a supply chain scenario, understanding delays, warehouse locations, and inventory attributes is crucial, but often omitted in basic control-flow analysis. Process mining, as it stands today, is primarily based on computational techniques and algorithms to analyze and optimize processes. Methods such as process discovery, conformance checking, and performance analysis rely on algorithmic approaches to extract patterns, detect deviations, and identify inefficiencies from event logs. Although these techniques have proven effective in providing data-driven insights, they often fail to use visualization as a powerful means of interpretation and communication. Most process mining tools provide static and linear representations, such as process models (DFGs, BPMN) or Gantt charts, which are primarily suited for experts familiar with these abstractions. This limited use of visualization overlooks the potential for dynamic, interactive visual tools that can make complex process data accessible to a broader audience, enabling intuitive exploration and deeper understanding of multi-faceted process attributes, such as time, resources, and contextual dependencies and correlations.
In contrast, the field of visual analytics focuses on developing tools and techniques to support the interpretation of complex, multi-dimensional data. It provides concepts and techniques to explore and analyze event sequences, allowing patterns and anomalies to emerge intuitively. For example, visualizations such as timelines, Sankey diagrams, or network diagrams make it possible to uncover dependencies and bottlenecks in event sequences. Despite their shared goal of deriving actionable insights from event sequence data, process mining and visual analytics have largely operated in parallel, leaving significant opportunities for synergy unexplored.
By integrating the two domains, process mining can benefit from visual analytics’ expertise in handling multi-faceted data. Visualizations that incorporate temporal, spatial, and relational aspects can unlock deeper insights into processes. Conversely, visual analytics can leverage process mining techniques to enhance guidance and evaluation during data exploration. For instance, capturing event logs while users interact with visualization tools can provide valuable feedback to refine analytical techniques.
The challenge lies in merging these disciplines effectively, combining their strengths to fully realize the potential of multi-faceted data for visual process analytics. This project tackles this open problem, aiming to create a unified framework that harmonizes these complementary fields, transforming the way we analyze and visualize complex processes.
In this project, the focus is on developing Visual Analytics techniques for event sequence exploration and analysis to enable Multi-Faceted Visual Process Analytics. The project is performed within the Visualization cluster under the supervision of dr.ir. Stef van den Elzen and dr. Fernando Paulovich.
The visualization cluster ( https://research.tue.nl/en/organisations/visualization-3 ) at TU/e has a strong track record in visualization and visual analytics for 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 – now UiPath and SynerScope); and a number of techniques that are used on a large scale worldwide.
Please note
You can apply online. We will not process applications sent by email and/or post.
A pre-employment screening (e.g. knowledge security check) can be part of the selection procedure. For more information on the knowledge security check, please consult Chapter 8 of the National Knowledge Security Guidelines( https://open.overheid.nl/documenten/ronl-5379d1b4f8b9784bf518251032507a965be9c92d/pdf ).
Please do not contact us for unsolicited services.
Type of employment: Temporary position
Contract type: Full time
Salary: Scale P
Number of positions: 1
Full-time equivalent: 1.0 FTE
City: Eindhoven
County: Noord-Brabant
Country: Netherlands
Reference number: 2026/271
Published: 2026-05-04
Last application date: 2026-06-04
埃因霍温理工大学 PhD position in Multi-Faceted Visual Process Analytics项目有没有奖学金，是不是全奖Phd招生，下面我们一起看一下【大学名称】Phd的奖学金资助情况
项目资助情况
Conditions of Employment
A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station. In addition, we offer you:
Full-time employment for four years, with an intermediate assessment after nine months. You will spend a minimum of 10% of your four-year employment on teaching tasks, with a maximum of 15% per year of your employment.
Salary and benefits (such as a pension scheme, paid pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labour Agreement for Dutch Universities, scale P (min. € 3,059 - max. € 3,881).
A year-end bonus of 8.3% and annual vacation pay of 8%.
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( https://www.tue.nl/en/education/graduate-school/phd/life-as-phd/#c324601 ).
An excellent technical infrastructure, on-campus children's day care and sports facilities.
An allowance for commuting, working from home and internet costs.
A Staff Immigration Team( https://www.tue.nl/en/working-at-tue/internationals ) and a tax compensation scheme (the 30% facility) for international candidates.
埃因霍温理工大学Phd申请条件和要求都有哪些？PhD position in Multi-Faceted Visual Process Analytics项目是不是全奖？有没有奖学金？下面我们一起看一下埃因霍温理工大学申请Phd直招需要具备哪些条件和要求，以及托福、雅思语言成绩要到多少才能申请。
申请要求
Job Requirements
We are looking for a candidate who meets the following requirements:
You are enthusiastic about research in Visual Analytics, Event Sequence Visualization, and Process Mining.
You have experience with or a strong background in Visualization or Visual Analytics. Preferably you finished a master’s in Computer Science, (Applied) Mathematics, or Electrical Engineering.
Expertise in the field of Process Mining/Event Visualization is a plus but not mandatory.
You have strong programming skills in object-oriented GUI languages (Qt/C++, Java, Python,...)
You have good communication skills and are able to work in a multidisciplinary team.
You are creative, critical, analytical, hardworking, and persistent.
You have a good command of the English language (knowledge of Dutch is not required).
报名方式
申请链接
招生人信息
Stef van den Elzen
邮箱：s.j.v.d.elzen@tue.nl
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