COLCOM - Postdoctoral Fellow in Machine Learning

Organisation/Company MOHAMMED VI POLYTECHNIC UNIVERSITY Research Field Mathematics Researcher Profile Recognised Researcher (R2) Established Researcher (R3) Application Deadline 11 Mar 2026 - 00:00 (UTC) Country Morocco Type of Contract Other Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

Years of Experience: 0 to 4 years

Education Level: Doctoral degree

Type of Contract: 1 year contract (renewable)

Number of Positions: 2

Expected Start Date: February 1st, 2026

About the University:

Located at the heart of the future Green City of Benguerir, Mohammed VI Polytechnic University (UM6P), a higher education institution with an international standard, is established to serve Morocco and the African continent. Its vision is honed around research and innovation at the service of education and development. This unique nascent university, with its state-of-the-art campus and infrastructure, has woven a sound academic and research network, and its recruitment process is seeking high quality academics and professionals in order to boost its quality-oriented research environment in the metropolitan area of Marrakech.

About the School

The College of Computing (UM6P-CC) is located in the future Green City of Benguerir. It provides world-class university education in computer science promoting discovery and innovation. The College currently offers an engineering degree in computer engineering, and a doctoral program in computer science.

About the Group

The Data Intelligence Group at UM6P-CC is a growing and supportive team with internationally recognized expertise in data management and machine learning. The group has a strong network of national and international collaborators in both academia and industry.

Job Title

Postdoctoral Fellow – College of Computing

Job Description

The Data Intelligence Group at UM6P-CC is seeking two postdoctoral fellows in machine learning who will support the group’s projects in the rapidly growing field of multimodal representation learning and retrieval.

The specific responsibilities of the postdoctoral fellow will include:

• conduct scientific research in the field of machine learning.
• develop novel machine learning algorithms primarily for representation learning, dimensionality reduction, clustering and search.
• conduct theoretical and experimental performance analyses of the proposed algorithms.
• publish research results in high-quality venues (conferences and journals) and present them in national and international scientific events and conferences.
• contribute to the supervision of undergraduate and graduate students.
• assist in writing research proposals.
• contribute to training and teaching activities, including the development of new courses.
• assist in the organization of workshops, seminars and other national and international events related to machine learning.
• work effectively with the DMG team and help nurture internal and external collaborations with academic and industrial partners.

Qualifications

• Ph.D. in Computer Science, Applied Mathematics, or a related field.
• Strong publication record in machine learning, with preference for expertise in representation learning, deep embeddings, contrastive learning, or foundation models.
• Solid background in linear algebra, probability, and statistics.
• Strong programming skills with deep learning frameworks (e.g., PyTorch) and standard data analysis tools.
• Familiarity with data structures, algorithms, and—ideally—search or indexing techniques.
• Excellent communication skills and strong analytical, teamwork, and organisational abilities.

Application:

• Applications should be submitted online and emailed to with the subject "(Postdoctoral Fellow Position)".
• Ph.D students in their final year of study are also encouraged as long as they expect to obtain their degree by September 2026.
• Emailed application materials should contain an up-to-date CV and a zipped archive which includes a cover letter and full transcripts. The CV should clearly indicate the following information: type of high-school degree obtained (e.g., Sciences Mathematiques A), overall high-school average, Mathematics grade in the national exam, and overall average/ranking in university studies.