Postdoc in Machine Learning

King Abdullah University of Science and Technology
July 09, 2024
Offerd Salary:Negotiation
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King Abdullah University of Science and Technology: Postdoc Positions:

Biological and Environmental Science and Engineering Division (postdoc)


Thuwal, Saudi Arabia

Open Date

Jan 25, 2023


The Machine Learning (ML) group led by Professor Ricardo Henao at KAUST (King Abdullah University of Science and Technology) has multiple openings for Postdoctoral level positions to conduct research in the field of machine learning, more specifically, deep learning and representation learning architectures. Application areas of ML include, but are not limited to, computer vision, natural language processing, computational biology and healthcare.

Our group focuses on important and fundamental open problems in machine learning research and challenging applications in diverse fields ( e.g. , biology and healthcare) where deep learning architectures, hierarchical learning models and representation learning can be truly impactful. The group strives to publish in top-tier ML venues such as NeurIPS, ICLR, ICML, AISTATS, CVPR, ICCV, ACL, EMNLP, etc. Our group is embedded in the BESE (Biological and Environmental Science and Engineering) division, which supports and promotes a rich multi-disciplinary work environment. We are looking for like- minded scientists who are passionate about ML methodology and/or its potentially transformative applications in research and industry.

The positions are fully funded and for postdocs, initially available for up to three years with the possibility of renewal.


King Abdullah University of Science and Technology (KAUST) is established in Saudi Arabia, on the Red Sea coastal area of Thuwal, as an international graduate-level research university dedicated to inspiring a new age of scientific achievement that will benefit the region and the world. As an independent and merit-based institution and one of the best-endowed universities in the world, KAUST intends to become a major new contributor to the global network of collaborative research. KAUST has quickly become one of the fastest-rising universities for high-quality research output, ranking as number six of the Nature Index's leading 150 young universities by article share in 2020. With a strong stand in the STEM disciplines, KAUST fosters collaborative and interdisciplinary research with an infrastructure for biotechnology entrepreneurship. The University was built as an urban vision and has already recruited world-class students, faculty, and researchers from over 100 nationalities.

The culture at KAUST embraces a healthy work and life balance. The university campus is located on the Red Sea's shores, built to protect the fragile coastal ecosystem, which includes a coral reef. KAUST's campus sets exceptional standards for residences and recreational facilities. Expats at KAUST are very well looked after, and postdoc packages include free accommodation, relocation, annual flights, and various additional incentives (see postdoc life at KAUST for further information).

The admission of students, the appointment, promotion and retention of faculty and staff and all the educational, administrative and other activities of the University is conducted with emphasis on equality, without regard to race, color, religion or gender. Further information can be found at (National Geographic): Episode 1, 2, 3 and 4.


The positions are suited for recent PhD graduates in relevant quantitative fields ( e.g. , electrical engineering, computer science, statistics and data science), with evidence of research activity resulting in publications and presentations. Effective communication skills in English both in writing and presentation are key for dissemination purposes.

Advantageous skills:

  • Experience with computational frameworks for machine learning ( e.g. , PyTorch and TensorFlow).
  • Experience with high-performance computational environments (e.g., cloud and clusters).
  • Experience with handling large-scale datasets.
  • Solid background in statistics and probability theory.
  • Prior publications in top-tier ML venues (see examples above).
  • Application Instructions

    Application should include:

  • Single page cover letter describing experience and research interests.
  • CV including a list of publications and previous machine learning experience.
  • Names and contact information for two references.
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