At the Center for AI Value, part of the Technology Marketing Group at ETH Zurich, we investigate how organisations can turn generative and multimodal AI into measurable value. Our current focus is one of the most consequential open questions in this space: how customer-facing AI should be designed when it speaks to real customers on behalf of a real firm. Generative voice agents now handle substantive service conversations, hand customers over to human employees, and generate content that reaches large professional audiences — yet the design decisions behind them are made largely by intuition.

We are looking for a doctoral candidate to help change that. The position is built around three projects, all of them conducted with industry partners and all of them designed to move from controlled experiments into live operations. You will study how tightly a firm should prescribe what its AI agent says, what happens emotionally and informationally when a conversation is handed from an AI to a human colleague, and how multimodal models can predict the performance of marketing content before it is published.

Research at the Center builds on the group's track record in data science, conversational AI, and marketing analytics and on substantial investments in on-premises GPU infrastructure that allows us to work with confidential partner data under full data control. The focus of the position is on research, aiming at publications in leading international management journals. The starting date is flexible. Applications will be accepted until the position is filled. For further information please refer to Sebastian Tillmanns.

The position spans three projects, each carried out in close collaboration with an industry partner and each combining controlled experimentation with access to real operational settings. The projects build on established collaborations, ongoing research, and technical infrastructure already in place at the Center.

Prompt granularity in AI voice agents. Firms deploying generative voice agents must decide how tightly to prescribe what the agent says. Tight specification buys reliability, completeness and policy compliance; open specification buys adaptivity to cases nobody anticipated. This project treats that decision as the machine analogue of the classic scripting-versus-empowerment trade-off in service operations — with the crucial difference that script strictness, an aspiration with human employees, becomes an enforceable parameter with a large language model. The programme comprises laboratory experiments with live voice interaction, a comparison of adaptive prompting policies, and a field experiment in the live call operations of a partner in the insurance sector. Outcomes are deliberately behavioural: verified task success, conversational repair derived from transcripts, and compliance failures such as unauthorised commitments.

Emotional grounding in AI-to-human handovers. When a customer in a negative emotional state is transferred from a voice agent to a human employee, does the emotional acknowledgement offered by the AI carry over — or does it retrospectively read as a script if the human does not build on it? This project examines whether the two agents jointly establish common ground about the customer's situation, and how that perceived continuity shapes whether customers feel heard and go on to recommend the provider. It progresses from a factorial experiment through studies with trained actors simulating the handover to a field implementation with the same insurance partner.

Multimodal prediction of marketing content performance. Building on an ongoing doctoral project and an established data collaboration in industrial technology, this project develops and benchmarks multimodal embedding approaches, text and image, encoded separately or jointly, as inputs to models that predict the engagement a social media post will generate before it is published. The successful candidate will work closely with the doctoral researcher already active in this research stream.

Together, the projects give the successful candidate something rare: theoretically grounded experimental questions, partners willing to randomise in live operations, and infrastructure that makes working with sensitive conversational data possible.

The successful candidate will conduct original research leading to publications in top-tier international journals. Responsibilities include:

• Designing and running controlled experiments, including studies with live voice interaction, and preparing them for pre-registration
• Using and extending the Center's existing experimental infrastructure for these studies, including prompt design as a controlled manipulation, generative voice agent pipelines, conversation logging and transcription
• Extending and validating existing LLM based coding pipelines for transcript derived measures, including validation against human coded gold standards
• Analysing experimental and operational data using appropriate causal and psychometric methods
• Preparing and coordinating field experiments within established industry collaborations and in close coordination with senior researchers, including feasibility assessment, randomization design, and retrieval of operational outcomes
• Working with multimodal embedding models and machine learning methods for content performance prediction
• Presenting results at international conferences and to partner organisations
• Contributing to teaching and supervising Master's theses within the Center's focus areas, alongside a limited share of administrative duties

• Educational Background: Applicants must possess a Master of Science (MSc), Master of Arts (MA), or Master of Philosophy (MPhil) degree in business administration, computer science, or related fields. This includes, but is not limited to, psychology, mathematics, engineering, physics, or statistics. Candidates from technical backgrounds are very welcome, but should be motivated by managerial and behavioural research questions rather than by model development as such. A demonstrated interest in management-related disciplines is highly desirable.
• Geographical Eligibility: We are seeking candidates who are currently residing in Europe.
• Research Interest: A strong interest in experimental research and quantitative empirical methods is vital.
• Organizational Skills: Candidates should exhibit excellent organizational skills, including the ability to manage and prioritize multiple tasks effectively.
• Communication Proficiency: Strong communication and writing skills in English are required, enabling effective collaboration and the preparation of scientific publications and reports.
• Language Requirement: Fluency in both spoken and written English is mandatory.
• Personal Attributes: The ideal candidate will take initiative, be results-oriented, organized, and demonstrate creativity in problem-solving.

Given the methodological breadth of the projects, we do not expect applicants to already be experts in every method used across the three research streams. We are looking for candidates with strong empirical foundations, intellectual flexibility, and the ability to develop additional methodological skills during the doctoral. Applications from members of underrepresented groups in academia are specifically encouraged.

The Center for AI Value at ETH Zurich, part of the Technology Marketing Group, offers an unusual combination for a doctoral researcher: theoretically ambitious questions, established collaborations with industry partners who are prepared to open their live operations to randomised studies, and the infrastructure to do the work properly. Our research is deeply rooted in ETH's commitment to acting responsibly and innovating for societal benefit, and our team is dedicated to publishing in esteemed journals while fostering a culture of inclusivity and well-being. The doctoral researcher will receive close scientific guidance from PD Dr. Sebastian Tillmanns and Dr. Joseph Ollier and will be embedded in the broader Technology Marketing Group led by Prof. Florian von Wangenheim.

We offer competitive one-year contracts, extendable to six years, complemented by generous travel and research allowances. New team members can count on the support and experience of colleagues working across AI in customer service, multimodal data modeling, and process automation, as well as on established experimental infrastructure for voicebot and chatbot research. Our substantial investment in on-premises GPU infrastructure gives our researchers state-of-the-art resources for LLM training and inference — and, equally important, keeps confidential partner data within our own secure facility, which is what makes research on real customer conversations possible in the first place. There will also be opportunities to become involved in grant applications building on the results of the doctoral research.

In line with our values, ETH Zurich encourages an inclusive culture. We promote equality of opportunity, value diversity and nurture a working and learning environment in which the rights and dignity of all our staff and students are respected. Visit our Equal Opportunities and Diversity website to find out how we ensure a fair and open environment that allows everyone to grow and flourish. Sustainability is a core value for us – we are consistently working towards a climate-neutral future.

We look forward to receiving your online application with the following documents:

• Cover letter motivating your interest in the position, including two short paragraphs on which of the three projects interests you most and where you see its main challenges
• CV
• University diplomas and transcripts

Further information about the Chair of Technology Marketing can be found on our website. Questions regarding the position should be directed to Sebastian Tillmanns, [email protected] (no applications).

Please note that we exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered.

We would like to point out that the pre-selection is carried out by the responsible recruiters and not by artificial intelligence.

ETH Zurich is one of the world’s leading universities specialising in science and technology. We are renowned for our excellent education, cutting-edge fundamental research and direct transfer of new knowledge into society. Over 30,000 people from more than 120 countries find our university to be a place that promotes independent thinking and an environment that inspires excellence. Located in the heart of Europe, yet forging connections all over the world, we work together to develop solutions for the global challenges of today and tomorrow.