Research Associate – AI-Data Science focus Inventory Optimization & Recommender Syste

Overview

University of Doha for Science and Technology (UDST) is the first national applied University in the State of Qatar, offering applied bachelor’s and master’s degrees in addition to certificates and diplomas in various fields. UDST has over 50 programs in the fields of Engineering Technology and Industrial Trades, Business Management, Computing and Information Technology, Health Sciences, Continuing and Professional Education and more.

With more than 700 staff and over 8,000 students, UDST is the destination for top‑notch applied and experiential learning. The University is recognized for its student‑centred learning and state‑of‑the‑art facilities. Our faculty are committed to delivering pedagogically‑sound learning experiences with the incorporation of innovative technological interventions, to further enhance students’ skills and help develop talented graduates that can effectively contribute to a knowledge‑based economy and make Qatar’s National Vision 2030 a reality.

We are seeking a highly motivated Research Associate to join a collaborative research project between the University of Doha for Science and Technology (UDST) and a leading technology‑driven business in Qatar. The ideal candidate will have a strong background in reinforcement learning, optimization, recommender systems, and applied machine learning, with a focus on developing advanced algorithms for inventory management and personalized customer experiences.

Key Responsibilities:

1. Development of an Inventory Optimization Module

• Conduct research on safety stock management and reinforcement learning (RL) approaches.
• Prototype RL‑driven inventory optimization algorithms.
• Integrate inventory optimization models with demand forecasting systems.

2. Recommender System Development

• Develop context‑aware recommender systems using knowledge graphs and session encoding techniques.
• Prototype and validate recommender algorithms for personalized recommendations.
• Integrate recommender models with inventory and forecasting systems.

3. Microservices, MLOps & Deployment

• Deploy RL optimizers and recommender systems as microservices.
• Build CI/CD pipelines for scalable and reliable deployment.
• Implement monitoring dashboards, drift detection, and performance alerts.

4. Documentation, Training & Academic Output

• Prepare technical documentation, API specifications, and system user guides.
• Contribute to case studies, final reports, and academic publications/patents.

Qualifications:

• Educational Background:
  • Ph.D. in Computer Science, Operations Research, AI, or a related field, OR
  • Master’s degree in Computer Science/Engineering with at least 3 years of experience in optimization, recommender systems, or reinforcement learning.
• Technical Expertise:
  • Strong knowledge of reinforcement learning, optimization algorithms, and recommender systems.
  • Experience with simulation and A/B testing methodologies.
  • Familiarity with knowledge graphs, session‑based recommendation, and personalization techniques.
  • Proficiency in deploying ML models as microservices and applying MLOps best practices
• Programming
  
  Skills:
  
  
  • Strong programming skills in languages such as Python or R.
  • Familiarity with machine learning libraries and frameworks (e.g., Scikit‑Learn, Tensor Flow, PyTorch) is a must.
• Analytical Skills/ Communication
  
  Skills:
  
  
  • Ability to design, prototype, and validate optimization and recommendation algorithms.
  • Strong documentation and presentation skills for both technical and non‑technical stakeholders.
• Team
  
  Collaboration:
  
  Proven experience working in collaborative team environments and managing multiple tasks effectively.