Fair Machine Learning in Marketing Fellowship applications are now open for an exciting PhD opportunity at Erasmus University Rotterdam, offered within a dynamic and internationally recognized research environment. This fellowship focuses on advancing fair and responsible machine learning practices in marketing, addressing critical issues such as algorithmic bias, ethical decision-making, and their broader societal impact through rigorous empirical research.
The fellowship is ideal for candidates with a strong quantitative academic background, including degrees in econometrics, computer science, statistics, mathematics, or related disciplines. Applicants should have solid programming experience in Python and/or R and a demonstrated interest in algorithmic fairness, ethics, and socially responsible data-driven marketing. A passion for applying machine learning to real-world marketing challenges will be considered a strong advantage.
Benefits of Fair Machine Learning in Marketing Fellowship
- Competitive monthly salary (€3,059 – €3,881) based on experience
- Full-time PhD employment (1.0 FTE)
- Access to world-class research facilities and high-performance computing
- Funding for international research visits (3–6 months) at leading global universities
- Strong supervision by leading scholars and integration into a top-ranked marketing research group
- Excellent career prospects in academia and industry
Eligibility for Fair Machine Learning in Marketing Fellowship
- Eligible Countries: All countries
- Acceptable Course or Subjects: The scholarship will be awarded in Marketing with a strong focus on Machine Learning, Causal Inference, and Algorithmic Fairness at Erasmus University Rotterdam.
- Admissible Criteria: To be eligible, applicants must meet the following criteria:
- Hold a quantitative MSc or Research MSc (e.g., econometrics, computer science, statistics, mathematics, economics, or related fields)
- Strong programming skills in Python and/or R
- Demonstrated interest in algorithmic fairness, ethics, and societal challenges
- Strong interest in marketing applications and real-world business problems
- Excellent written and spoken English proficiency
- High motivation to pursue an international academic career
- Strong analytical skills, intellectual curiosity, and commitment to scientific integrity
- Ability to work independently and collaboratively in an interdisciplinary environment.
How to Apply for Fair Machine Learning in Marketing Fellowship
- Apply online through the ERIM (Erasmus Research Institute of Management) application system
- Prepare all required documents as listed on the ERIM website
- Follow the official application guidelines carefully
- Submit your application via the official link:
Frequently Asked Questions
The Fair Machine Learning in Marketing Fellowship is a funded PhD to study algorithmic bias and fairness in targeted marketing using causal machine learning. You must have quantitative skills, engage in research, take tailored courses, get salary and support, publish work, and apply by the deadline.
What is the Fair Machine Learning in Marketing Fellowship about?
The fellowship is a fully funded PhD position to explore algorithmic bias and fairness in targeted marketing campaigns. You will develop methodological frameworks using causal machine learning, study discrimination, work on real field experiments, and collaborate with faculty and industry partners.
Who can apply for the Fair Machine Learning in Marketing Fellowship?
This fellowship seeks candidates with strong quantitative backgrounds like computer science, statistics, or econometrics. You should have excellent English skills, programming experience (e.g., R or Python), and a deep interest in societal problems, fairness, and ethics in automated marketing systems.
What will I research in the Fair Machine Learning in Marketing Fellowship?
You will examine how algorithms create or amplify biases in marketing, combine field experiment data with simulations, and design fair personalized marketing policies. The project draws on active learning, causal inference, and policy evaluation to address real managerial and societal challenges.
What skills will I gain during this PhD fellowship?
You will strengthen skills in causal machine learning, simulations, data analysis, programming, research communication, open science practices, and interdisciplinary collaboration, gaining experience presenting at conferences and preparing work for publication.
What kind of support does the fellowship provide?
You receive a competitive salary, benefits under a Dutch university agreement, access to high-performance computing and research facilities, and funding for a 3 to 6-month international research visit to expand your academic network.
What courses are part of the PhD training?
During the fellowship, you typically take courses in machine learning, econometrics, causal inference, statistics, and quantitative marketing. The exact coursework is tailored with advisors to build expertise relevant to your research path.
How does this fellowship address societal impact?
The project encourages research that improves consumer wellbeing and tackles algorithmic discrimination in marketing. It aligns with broader goals to be a force for positive societal change by promoting fairness and openness in algorithmic decision systems.
Where is the Fair Machine Learning in Marketing Fellowship hosted?
This PhD position is hosted by the Rotterdam School of Management at a major European university. You will join a diverse, collaborative marketing group with world-class research output and access to seminars, workshops, and academic events.
What are the expected outcomes of the fellowship?
You are expected to produce research publishable in top marketing and computer science journals, create open-access tools and tutorials, and complete a PhD dissertation that contributes to fairness, causal inference, and marketing science.
How can I apply for the fellowship?
Apply by submitting required documents before the deadline specified in the vacancy. Direct questions about admissions to the designated email. Successful candidates become salaried employees while pursuing their PhD research.
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