Postdoctoral Position in the Intersection of Machine Learning and Optimization

[Morteza H. Chehreghani]

Project Title: Learn2Opt — Learning to Optimize: A Sustainable Approach

Host Institution: ESSEC Business School
Location: Paris Area, France
Starting Date: September–November 2025 (flexible)
Duration: 18 months (1.5 years), with a possible extension of up to 1 additional year

Project Summary:

The proposed research aims to develop innovative, efficient, and sustainable techniques for optimization enhanced by machine learning (ML), focusing on decomposition methods such as column generation and Benders decomposition. These methods are essential for solving complex large-scale decision-making problems, but their high computational demands often limit their practical application. A major challenge in current methodological evaluations is the neglect of the time and effort required to train ML models. In practice, training these models can require significant computational resources—often thousands of GPU hours—resulting in high energy consumption and substantial carbon emissions.

This project seeks to address this gap by integrating Active Learning (AL) and Reinforcement Learning (RL) to improve the efficiency and effectiveness of optimization processes. AL will reduce training times by focusing on the most informative data, while RL will dynamically guide the optimization process. By explicitly incorporating training effort as a key evaluation criterion alongside solution quality, the project aspires to establish a more comprehensive and sustainable standard in the field.

To validate our approach, we will use the Stochastic Dial-a-Ride Problem (SDARP) as a test case. SDARP involves optimizing vehicle routing under uncertain, real-time conditions, reflecting the complexities of real-world applications such as urban transportation systems. 

Position Description:
 We are seeking a highly motivated postdoctoral researcher with expertise in mathematical optimization and machine learning. The successful candidate will work on designing and implementing ML-augmented optimization frameworks, with a particular focus on integrating machine learning models into decomposition techniques and developing data-efficient training strategies based on active learning. The position also involves contributing to the definition of benchmark problems and the evaluation of novel methodologies on both synthetic and real-world instances. 
 
Main Tasks:
- Develop efficient ML models to approximate subproblems in decomposition-based algorithms
- Integrate Active Learning (AL) techniques to reduce the training dataset size
- Implement and benchmark optimization pipelines on real and synthetic instances of SDARP
- Collaborate with international experts and contribute to scientific publications

Candidate Profile:
- PhD in Operations Research, Machine Learning, Applied Mathematics, Computer Science, or a related field
- Experience with mathematical optimization and machine learning
- Knowledge of decomposition methods, active learning, or reinforcement learning is considered a plus
- Excellent coding skills (e.g., Python, Julia)
- Excellent written and oral communication skills in English

Advisory Team:
- Prof. Emiliano Traversi (ESSEC Business School, France)
- Prof. Morteza Haghir Chehreghani (Chalmers University of Technology / University of Gothenburg, Sweden)
- Prof. Ashkan Panahi (Chalmers University of Technology / University of Gothenburg, Sweden)

How to Apply:
Send your application to emiliano...@essec.edu with the subject "Postdoc Application - Learn2Opt".
Your application should include:
- A detailed CV
- A short motivation letter (1 page)
- Names and contacts of two references
- (Optional) up to two representative publication

The position will remain open until filled.

ESSEC Business School is an equal opportunity employer and values diversity in its workforce.