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The PhD program

PhD Candidates develop their precise dissertation projects jointly with their advisor within the first months of doctoral studies. Roughly speaking, these PhD projects can be assigned to three thematic areas: modeling cognition and social cognition, modeling motivation and affect, and modeling individual differences. Within each thematic area two complementary and intertwined approaches are possible: Projects focusing primarily on model developments and statistical refinements (model development perspective) and projects aiming at innovative applications of existing models to new research questions (model application perspective). PhD Candidates will profit from diverse opportunities to discuss their PhD projects with fellow PhD Candidates, researchers from the SMiP group as well as external experts.

Modeling cognition and social cognition

Cognition is a summary term for all processes that serve our knowledge about the world.

Modeling motivation and affect

Affective states like feeling happy, relaxed or fatigued fluctuate substantially within individuals due to influences such as diurnal rhythms, daily events, and self-regulatory behavior.

Modeling individual differences

Interindividual differences in personality attributes and cognitive performance are at the core of many domains of psychological research.

To support and accelerate the PhD phase, the SMiP group offers a unique qualification program. It comprises 6 semesters and includes different types of teaching activities covering, for example, core courses in statistical modeling, skill trainings (e.g., academic writing and publishing), and workshops on diverse topics given by members of the SMiP group or renowned international experts from partner institutions. In addition to the course work, active participation in teaching and in conferences will be supported. Furthermore, PhD Candidates will have the opportunity to visit external labs in research groups abroad.

Overview Qualification program

The PhD Candidates' training program covers 6 semesters and includes four types of teaching activities: core courses, training of key competencies, workshops, and colloquia.

Spring Summer 2021

Workshop topics in this semester cover, e.g. “Multiverse Analysis”, “Basic Concepts of Stochastic Processes”, “Hands-on Open Science”, “Two-Process-Theories: An overview”, and “Multilevel Structural Equation Modeling”.

Fall Winter 2021

Workshop topics in this semester include, e.g. “Introduction to Bayesian Modeling”, “Hypothesis Evaluation Using the Bayes Factor”, “Bayesian Cognitive Modeling”, and “Methodology of Replication Studies”.