PostDoc: Personalized Visual Analytics

The Interactive Visual Data Analysis (IVDA) Group at the University of Zurich is seeking a talented Post-Doc (m/f/d) at the intersection of visual analytics, interactive machine learning, and human-centered AI. Together, we will develop novel approaches for the characterization, design, and evaluation of interactive visual data analysis solutions that combine the strengths of both humans and algorithms. This position is particularly suitable for candidates with interests/experience in human preference elicitation and multi-criteria decision support, which we will study for the case of item ranking challenges.

Multi-criteria decision support through item ranking is an understudied research direction. Current solutions exhibit considerable limitations, primarily due to their inability to meet the multi-faceted nature of human decision-making. Choosing between thousands of relevant items is a common task, where people are left alone with the need to express and balance multiple desirable preferences at once. Item rankings are a popular and universal approach to structuring unorganized item collections by multiple criteria at the same time. In interactive solutions, people can express multiple preferences, used by algorithms to compute human-centered and personal rankings. Visual Analytics (VA) is a field of research that supports complex human decision-making tasks by bringing together human intellectuality with the computational power of algorithms in effective human-in-the-loop approaches.

In this project, we will demonstrate how cutting-edge VA principles can be transferred to item ranking, to overcome remaining challenges, demonstrate effective and efficient interactive ranking creation solutions, and empower broad audiences. Limitations of ranking systems mainly include their incompatibility for the elicitation of item- and attribute-based preferences, insufficient handling of user-introduced uncertainty, inadequate guidance for engaging with large item sets, lacking interaction histories, inefficient human-centric feedback loops, or a lack of explanations and transparency of ranking algorithms, thus creating a disconnect between the technology and nuanced user needs. Finally, interactive ranking creation does not yet make use of interfaces for eliciting implicit user preferences, e.g., in textual form using large language models.

Link | see our research page for baseline work and preliminary results on Item Ranking

• Ph.D. degree, e.g., in Informatics, ML/AI, Data Science, or HCI.

• Strong publication record and ability to conduct scientific work and dissemination activities in the realm of visual analytics.

• Excellent communication and collaboration skills, and ability to work in interdisciplinary teams.

• Ability to independently teach university-level courses and supervise students.

• Extensive expertise in some of these areas: visual analytics, data science, information retrieval, data mining, machine/deep learning, AI, natural language processing, explainable AI, human-centered AI, responsible AI, algorithmic fairness, biases, transparency, HCI, applied research methods, or types of empirical research.

• Profound skills in programming in Python and supervise programming projects.

• Hands-on supervision/mentorship for further career development
• Support in conducting excellent research and in publishing results in top international journals and conferences
• Creative working atmosphere in a motivated, cooperative, and technically very well-equipped environment
• Possibility to work with several Ph.D. students in the lab in topics around IVDA, particularly on personalized VA for item ranking
• Excellent professional and personal development possibilities and hence, excellent basis for a future career in interactive visual data analysis in an industrial context
• Specific benefits like flexible working hours, young scientist promotion opportunities, parental leave benefits, nursery services, and care for dependents and much more
• Very good salary, according to local university regulations and standards in Switzerland

Official applications should include:

• Motivation letter
• Detailed CV
• Certificates of degrees and educational background
• Clear exposition of prior data visualization experience
• Copy of the Ph.D. Thesis

The UZH is a top internationally recognized research university with faculties in medicine, humanities, economics as well as mathematical and natural sciences. UZH is the largest university in Switzerland and regularly ranked among the top world leading research universities. The Department of Computer Science (Institut für Informatik – IfI) covers major computer science, software engineering and information management research and teaching topics, it offers B.Sc., M.Sc. as well as PhD degrees in informatics/computer science.

The Interactive Visual Data Analysis Group is located at the IfI, in the vibrant city of Zurich as part of the University’s new Nord-Campus in Oerlikon in a renovated modern office building. The UZH Nord-Campus is conveniently located a short walk off the Max-Bill Platz, center of the new trendy living, shopping and business district in Oerlikon, as well as near the Oerlikon train, S-Bahn and tram stations. The Zurich international airport (ZRH) is easily reachable with public or private transportation.

PostDocs are remunerated according to local university regulations and standards from the funding agencies. Appointments will be made with respects to standard University rules; same applies for fringe benefits and vacation days. Appointments are expected to involve a full-time effort in research, teaching, and administration. It is the goal of UZH to offer an equal opportunity workplace environment and as part of this, we especially encourage women to apply. Specific benefits include flexible working hours, young scientist promotion opportunities, parental leave benefits, nursery services, and care for dependents and much more.