Admission Procedure

The application deadline for admittance and matriculation in the following fall term is  December 1^st.  Candidates who are short-listed for potential admission will be contacted about interviews and subsequent steps before mid-January.

Applicants must obtain a master's degree before they are admitted to the doctoral program.
It is strongly recommended that applicants who did not receive their master's degree from a Swiss university take the GRE (Graduate Record Exam) General Test. Applications without GRE test results will be reviewed, but not submitting GRE test results will be considered a drawback and must be offset by very strong academic credentials (e.g., high grade point average, relevant research experience).

Proficiency in English is required. No specific test is required, but applicants who have taken the TOEFL (Test of English as a Foreign Language) are encouraged to provide a copy of the official test results.

Note:  Degrees from Master of Advanced Studies programs (e.g. MAS, EMBA, MBA, etc.) do not qualify for acceptance to our program.

Students are expected to devote themselves full-time to our PhD program.
You will be informed about the outcome of your application within six weeks after the deadline.

Applicants are only admitted to the Doctoral Program in Neuroeconomics if funding for their studies has been secured either by one of the chairs at the Department of Economics or by the applicant him/herself. If you have already received funding for your doctoral studies, please remember to include a copy of the funding approval in your application package.

Courses begin in September. 

Initial applications should be submitted (in electronic form only; one single PDF file per applicant) to the Doctoral Program Coordinator (dpneuro@econ.uzh.ch). These must include:

• a curriculum vitae
• a letter of motivation
• a transcript of MSc grades and diploma if available
• GRE score report
• TOEFL score report
• contact details of at least two referees

The following are brief descriptions of a few potential projects open to new PhD students. Please note that funding is not tied to specific projects, and we encourage PhD students to work closely with our faculty to develop their own tailored research projects.

Stick to the current course of action and exploit the rewards it provides or switch and explore other, potentially more rewarding options? This exploration-exploitation dilemma is one of the most basic dilemmas facing decision makers and its optimal solution requires a balance between the two strategies. However, multiple studies reported suboptimality in this type of decision-making in both healthy and clinical populations. In our project, we aim to elucidate the neural basis of behavioral flexibility and decision optimality and provide a knowledge basis for disorders affecting these functions. To this end, we plan to combine multiple techniques such as functional and structural magnetic resonance imaging and eye-tracking. Moreover, we plan to use pharmacological intervention to investigate a causal relationship between neurotransmitter systems in the brain, brain activity in predefined regions of interest and behavioral differences in resolving exploration-exploitation dilemma.

With climate change, heatwaves have become more frequent and intense, making heat an increasingly present stressor, with negative effects for physical and mental health. However, the impact of heat on behavior and in particular value-based decision-making remains poorly understood. By contrast, psychological stress has been extensively studied. While some evidence suggests that heat and psychological stress may engage distinct physiological mechanisms, direct comparisons are lacking. This project will compare neural and behavioral responses to heat stress, psychological stress, and a no-stress control. Participants will complete a set of decision-making tasks assessing the neural processing of reward magnitude and decision quality. Hormonal markers (cortisol and alpha-amylase), physiological signals, and subjective reports will also be measured at multiple time points. The project will uncover the neural impact of heat stress on decision-making and elucidate how value-based decisions are modulated by heat stress.

Human decision-making often departs from classical notions of rationality, exhibiting biases or following heuristics (e.g., risk attitudes captured by theories such as prospect theory). The underlying computational principles governing these behaviors remain incompletely understood. This project aims to develop and test mathematical frameworks rooted in information theory and the mathematics of machine learning to explain cognitive and behavioral phenomena observed in choice under uncertainty. By formalizing how limited information-processing capacity, noisy internal representations, and adaptive coding shape subjective valuation and risk preferences, we seek to build a unified theory of decision-making that bridges behavioral economics and computational neuroscience.
Candidates with a strong background in mathematical statistics, probability theory, and optimization are especially encouraged to apply. Experience with reinforcement learning theory, variational inference, or rate-distortion theory is a plus.

Attention and working memory constraints are limiting factors in many aspects of human behavior, including decision making. The aim of this project is to determine if an individual’s working memory capacity together with specific features of the choice problem compound the influence of visual inattention on choice accuracy. Empirical research has shown that visual fixations (a proxy for attention) play a causal role in determining the outcome of choices. This is true for both perceptual and value-based choices. Although most studies of the role of visual attention on choice have focused on group-level estimates, recent work has shown that there are substantial differences in the size of this fixation-dependent discount rate across people, and that individuals who discount unattended items more steeply make more suboptimal choices. However, the underlying reasons why fixation-dependent discount rates differ between individuals are unknown. This project will test the hypothesis that the influence of attention on choices is proportional to working memory capacity and load. Applicants who have experience with eye-tracking methods and Bayesian computational modeling are particularly encouraged to apply.

Understanding what others think, feel, or intend is essential for social life, cooperation, and conflict resolution. When this ability is impaired, as in autism spectrum or borderline personality disorders, social interactions can become difficult. Yet, we still know little about how the brain actually enables this ability. Existing studies often use simplified, static scenarios that fail to capture the dynamics of real human interaction.
This project combines modern neuroscience with machine learning to uncover how the brain processes social information. Using new, realistic experimental designs and recurrent neural networks, we will study the thoughts and intentions underlying cooperation and competition while recording brain activity with fMRI and testing causality through transcranial magnetic stimulation (TMS).
By linking these computational insights to brain mechanisms, and focusing on differences in people with high-functioning autism, we aim to deepen understanding of social cognition and lay the groundwork for improved diagnosis and personalized therapies for social-cognitive difficulties.

People often make choices that seem irrational: we take too many risks—or avoid them entirely—choose short-term rewards over better long-term outcomes, and let others’ behavior influence our own. Traditional theories in psychology and neuroscience explain such patterns as differences in motivation or personal preferences. Yet, these theories cannot fully explain why such behaviors arise or vary so much between people and situations.
This project will test an alternative perspective: Irrational choices may not only reflect motivation, but also distortions in perception and memory caused by the brain’s limited processing capacity.
To test this idea, the project will combine behavioral experiments, computational modeling, neuroimaging to measure how precisely the brain encodes information, and non-invasive brain stimulation to explore causal effects. It will also study individuals with dyscalculia, a condition affecting numerical processing, to see how reduced cognitive precision alters decision-making. By linking the accuracy of neural representations to real-world behavior, the project aims to uncover a biological explanation for why people differ in their risk-taking, impulsivity, and social choices.