Students (all Formats)

We are looking for motivated students (m/f/d) to collaborate with us in the areas of visual analytics, interactive data science, interactive machine learning, and human-centered artificial intelligence. Many of our goals align with the characterization, design, and evaluation of interactive visual interfaces to enable humans interacting with data. With our systems, we study innovative forms of human-machine collaboration for data analysis and data-driven decision-making. Our systems seek to combine the strengths of both humans and algorithms in interactive machine learning and data science applications.

As part of the Interactive Visual Data Analysis (IVDA) Group, you will work on a project/thesis/study in different graded formats, help develop/implement new algorithms and techniques in our group framework if you opt for an assistant (HiWi) position.

We collaborate with students in all typical forms of study and work

• Student Theses (Master Thesis, Bachelor Thesis)
• Master Projects (2-5 students)
• Independent Studies (3-9 ECTS)
• Student Assistants ("HiWis") (with B.Sc. / M.Sc. degree)

Our primary research focus is at the intersection between Information Visualization, Visual Analytics, Human-Computer Interaction, and Machine Learning. Your support is necessary to help the group follow a human-centered approach to data science, in order to foster the involvement of humans in the data analysis process with interactive visual interfaces. Your contributions in the IVDA group may align with one of the following problem areas of data science and machine learning / artificial intelligence:

• Data-oriented challenges, like complex data, heterogeneous data, multimodal data, dirty data, uncertain data, or unlabeled data.
• Important model-oriented challenges include data pre-processing, model building, model quality assessment, or model explanation.
• Particularly interesting design-oriented challenges are different degrees of user expertise, users’ personalization intents, and understanding and supporting user preferences with respect to data and tasks.
• Interesting human-machine collaboration aspects are along the lines of establishing novel forms of bidirectional communication between humans and the AI.

For more information about the history of IVDA research context, see juergen-bernard.de

• Self-driven and independent work style with an emphasis on open research challenges
• Interest in human-centered aspects of data science, machine learning, and AI
• Knowledge or interest in one or more of the following: information visualization, visual analytics, machine learning, data mining, information retrieval, or related data science fields
• For programmers: profound skills in programming
• For user study supporters: skills in experiment designs and in conducting user studies
• Interest to work on applied research questions in a collaborative research environment

Applications should include:

• Short informal motivation statement
• CV, certificates of study/educational background
• Exposition of data visualization experience
• Your expectations to the position, skills you want to learn