"Postdoctoral Researcher in Digital Twin for Planning Under Uncertainty"

Field: Mining Engineering, Industrial Engineering, Data Science

Duration: 12 months, renewable once.

Location: UM6P, Benguerir Campus

Preferred Start Date: As soon as possible

Context and Objectives

As part of the project "Digital Twin for Planning Under Uncertainty", we are seeking a postdoctoral researcher to develop a digital twin aimed at enhancing the planning of OCP’s production activities.

The primary objective is to design robust and efficient planning solutions—integrated within a digital twin—that account for the uncertainties and variability inherent in industrial processes. Machine learning techniques may be employed to support the modeling of uncertainty, along with the formulation and resolution of planning problems using stochastic optimization methods.

Key Responsibilities

The selected candidate will be expected to:

• Design and implement advanced decision-making model for mining activity planning, considering field constraints and performance objectives.
• Propose mechanisms for dynamically integrating uncertainty into planned decisions.
• Leverage real-time data from the industrial information system to adapt production plans.
• Collaborate with technical and data teams to integrate the models into a functional digital twin environment.
• Contribute to industrial validations, results presentations, and scientific publications.

Candidate Profile

PhD in Mining Engineering, Industrial Engineering, Applied Mathematics, Data Science, or related fields.

Strong background in Operations Research.

Experience with industrial data processing, particularly in dynamic, real-time environments.

Proficiency in machine learning techniques.

Ability to work in an interdisciplinary and collaborative environment.

Industrial experience or prior involvement in collaborative projects is an asset.

Application

Applications should include:

• A detailed CV
• A cover letter
• A list of publications
• References or recommendation letters