GSMI - Post-doc Position, Process Mineralogy, Mineral Processing, Artificial Intelligence

Discipline: Process Mineralogy, Mineral Processing, Artificial Intelligence

Duration: 12 months, with the possibility of renewal for an additional 12 months

Institutions: UM6P-Benguerir/ Mineral-X - Stanford University

Starting date: As soon as possible

We are seeking a Postdoctoral Fellowto join an exciting research project related to improving the recovery of phosphate from flotation tailings using process mineralogy. The successful candidate will have the opportunity to lead and conduct research at the intersection of process mineralogy, mineral processing, and artificial intelligence in a collaboration between Geology and Sustainable Mining Institute (UM6P, Morocco), and Mineral-X (Stanford University, USA).

Qualifications 

• PhD in process mineralogy, mineral processing, mining, chemical or geological engineering, or a PhD in a related field with mining/mineral processing experience.
• Knowledge of process mineralogy, froth flotation through past research, coursework, or job experience. 
• Proficiency in programming (Python, Julia) (provide evidence with specific examples).
• Experience with statistical modelling and experimental design. 
• Ability to work in a multidisciplinary team.
• Strong written and oral communication skills. 

1. Context and objectives

Process mineralogy is a discipline that has recently been used for diagnosing and improving ore processing flowsheets such as flotation. It is well known that flotation performance depends on several intrinsic and extrinsic parameters. The intrinsic parameters of the process include, among others, the type of reagents, their dosages, the solid concentration, and the air flow rate. However, the extrinsic parameters include the properties of the ore to be floated, namely its particle size distribution, the degree of mineral liberation, mineral associations, and the surface properties of the minerals. In general, a mineral cannot be floated even if the processing conditions are optimized if it is locked (encapsulated). To improve flotation recovery, regardless of process parameters, it is essential to conduct a physical, chemical, and mineralogical analysis of the tailings to study the distribution of phosphates and their relationships with gangue minerals such as silicates and carbonates.

2. Main tasks

The selected candidate will work on:

• Process-mineralogical characterization of the feed, concentrate, and flotation tailings.
• Testing the effectiveness of different grinding energies on phosphate liberation within flotation tailings.
• Developing an AI-driven intelligent framework to optimize grinding time to enhance phosphate liberation with minimum slime generation. 
• Design of laboratory flotation tests to optimize process parameters.

3. How to apply?

Candidates must upload the following documents via the recruitment platform:

• Cover letter summarizing your research interests and qualifications.
• CV including a list of publications and research projects.
• Contact information for 1 reference.