Program Overview

Introduction to the Doctoral Program in Information Systems

The doctoral program in Information Systems at HEC Lausanne is designed to help doctoral students develop their ability to conduct independent scientific research and enrich the body of knowledge in research areas closely related to new technologies and digital innovation.

Structure of the Program

The program is divided into two stages:

1. The first stage, which lasts up to 24 months, focuses on acquiring methodological skills and specific knowledge in the chosen research domain through a customized course program (minimum 18 ECTS).
2. The second stage is dedicated to research projects, where the doctoral student prepares submissions to international conferences and journals in the field of Information Systems and Computer Science.

Candidatures

Candidatures are accepted throughout the year, but candidates must obtain the agreement of a thesis supervisor before submitting their application. The candidate must hold a Master's degree (MSc) in Information Systems, Computer Science, or an equivalent degree (with a minor in Information Systems / minimum 30 ECTS).

Financement

There are essentially three ways to finance the doctorate:

• Assistantship
• Study grant
• Job/internship The tuition fees are very low (CHF 280.- for the first semester + CHF 80.- for each additional semester).

Doctorates in Progress and Past Theses

The program provides a list of current doctoral students and past theses, which can be consulted for more information.

Faculty and Research

Two committees are involved in the doctoral thesis: the Doctoral Commission and the Thesis Jury. The Doctoral Commission is responsible for evaluating the doctoral student's progress, while the Thesis Jury evaluates the final manuscript of the thesis.

Contact

For more information, please contact the program coordinator, Sarah Duplan, at the University of Lausanne, HEC Lausanne, Internef 234, Quartier Chamberonne, CH-1015 Lausanne.

Courses

The program offers various courses, including:

• Methodology of Research in Computer Science
• Theories and Methods of Information Systems
• Good Research Practices and Research Ethics
• Qualitative Research and Mixed Methods
• Experiments: Field, Lab, Natural, and Quasi-Natural
• Machine Learning in Management Research
• Data Science and Machine Learning

Course Descriptions

Methodology of Research in Computer Science

This course teaches students the basics of research methodology in computer science, including how to identify and critically read relevant research articles, extract and understand key innovations, and evaluate the scope and validity of these innovations.

Theories and Methods of Information Systems

This course provides an introduction to the fundamental aspects of Information Systems theories and methodologies, aiming to give students a broad knowledge base and understanding of the main theories used and developed in Information Systems research.

Good Research Practices and Research Ethics

This course focuses on the main perils that researchers may face during their career, including research ethics, scientific integrity, and reproducibility.

Qualitative Research and Mixed Methods

This course offers an introduction to qualitative and mixed methods, aiming to provide students with the necessary skills to design, execute, report, and examine qualitative and mixed methods research in the management field.

Experiments: Field, Lab, Natural, and Quasi-Natural

This course provides an in-depth understanding of causality in empirical research and explains why experiments are particularly useful for identifying causal relationships.

Machine Learning in Management Research

This course offers an advanced analysis of the multidimensional impact of Artificial Intelligence (AI) in the management research field, covering the technical foundations of machine learning and associated advanced technologies.

Data Science and Machine Learning

This course aims to understand the basic terminology of Data Science and Machine Learning, identify potential pitfalls, and acquire a general understanding of how to solve concrete problems using Python code.

FAQ

For frequently asked questions, please refer to the program's FAQ section, which provides detailed answers to common queries.