PhD in Data Science

University of Groningen; Groningen; On-site; EUR 30,000 - 42,000; Full time

Job summary

The PhD position at University of Groningen invites you to use data science to develop digital proxies for Reward, Motivation and Impulsivity and relate them to neurobiological markers across mental and metabolic health. You will integrate systematic evidence synthesis, data science and clinical validation in an international, interdisciplinary project.

As a PhD candidate, you contribute to establishing evidence for digital proxies and support precision-medicine insights that cut across diseases.

Qualifications

• Master's degree in a relevant field (e.g., data science, psychology, neuroscience) with strong analytical skills.
• Experience with data analysis, statistics, or machine learning is desirable.
• Ability to work across disciplines and international teams.

Responsibilities

• Systematically synthesize evidence on digital proxies related to RM&I.
• Develop and apply data-driven digital proxies in longitudinal datasets.
• Investigate relationships between digital proxies and neurobiological markers across conditions.

Skills

Data analysis

Analytical mind

Interdisciplinary

Education

Master's degree in a relevant field

Tools

Python

R

Data visualization

Job description

Are you interested in using data science to understand how digital measures can capture clinically meaningful aspects of reward, motivation and impulsivity? Do you want to develop and apply innovative digital proxies and combine them with neurobiological markers to advance precision medicine across mental and metabolic health conditions? Then this PhD position in an international pioneering project may be an excellent opportunity for you. You will work in an interdisciplinary, international research project aiming to advance a transdiagnostic precision-medicine framework centred on Reward, Motivation and Impulsivity. The project brings together expertise in longitudinal research, biomarker discovery, clinical validation, data science, evidence synthesis and stakeholder engagement. A central ambition is to move beyond disease-specific approaches by identifying biological, behavioural and digital markers that can help characterise clinically meaningful processes across different health conditions. As a PhD candidate, you will contribute to this ambition by helping establish the evidence base for digital proxies, developing and applying data-driven measures, and investigating their relationships with neurobiological markers.

What are you going to do?

We are looking for an ambitious and analytically minded PhD candidate to investigate digital proxies of Reward, Motivation and Impulsivity (RM&I) and their relationship with neurobiological markers and clinically relevant outcomes. Your work will be connecting systematic evidence synthesis, data science, longitudinal research and clinical validation. Your research will contribute to understanding whether and how digital measures can provide scalable and meaningful proxies for RM&I-related processes across major depressive disorder (MDD), Alzheimer's disease (AD), and obesity (OB).

Your PhD research will have three closely connected components:

Systematic review of digital proxies:

You will systematically synthesise existing evidence on digital proxies relevant to RM&I, identifying the types of digital measures that have been investigated, their relationship with behavioural and clinical constructs, and their potential relevance for transdiagnostic research.

Development and application of digital proxies:

You will use data-science approaches to develop, characterise and/or evaluate RM&I-related digital proxies. You will subsequently apply these digital proxies to analyses conducted within the project's longitudinal and clinical datasets, contributing to the identification of meaningful patterns across individuals and disease trajectories.

Hypothesis testing with neurobiological markers:

You will investigate relationships between digital proxies and neurobiological markers, testing hypotheses about the biological mechanisms underlying RM&I-related processes. Depending on the available data and the development of the research, this may involve integrating digital, behavioural, clinical and neurobiological measures.

An important aspect of your work will be to examine the clinical and transdiagnostic relevance of digital proxies. You will contribute to determining whether digital measures can complement existing biomarkers and endpoints and help identify measurable features of RM&I that are relevant across different conditions.

You will work closely with researchers involved in longitudinal cohort analyses, clinical studies, biomarker research and evidence synthesis. Your work will therefore sit at the intersection of data science, digital phenotyping, clinical research and neurobiology.