Postdoctoral Researcher – Groundwater Modelling & Machine Learning

University of Doha for Science and Technology (UDST) was officially established by the Emiri Decision No13 of 2022, and it is the first national university specializing in academic applied, technical, and professional education in the State of Qatar. UDST has over 70 bachelor's and master's degree programs, diplomas, and certificates. The university houses 5 colleges: The College of Business, the College of Computing and Information Technology, the College of Engineering and Technology, the College of Health Sciences, and the College of General Education, in addition to specialized training centers for individuals and companies. UDST is recognized for its student-centered learning and state-of-the-art facilities. Its world-renowned faculty and researchers work on developing the students’ skills and help raise well-equipped graduates who proudly serve different sectors of the economy and contribute to achieving human, social, and economic development goals nationally and internationally.

With more than 700 staff and over 8,000 students, UDST is the destination of choice for applied and experiential learning. The University is recognized for its student-centered learning and state-of-the-art facilities. Our faculty are committed to delivering pedagogically-sound learning experiences that incorporate innovative learning technologies.  Our aim is to enhance students’ skills and help develop talented graduates who can effectively contribute to a knowledge-based economy and make Qatar’s National Vision 2030 a reality.

The Applied Research, Innovation, and Economic Development Directorate at UDST invites applications for the position of Postdoctoral Researcher. This role is integral to the development of transient groundwater models that address Qatar’s unique environmental challenges and support sustainable water resource management.

Your Commitment

The successful candidate will play a crucial role in developing and implementing advanced, transient MODFLOW groundwater models specifically designed for Qatar’s aquifer systems. These transient models will dynamically simulate groundwater flow, levels, and quality, responding to temporal changes in input data and environmental conditions. This position requires strong expertise in Python programming, machine learning, and hydrological modeling. The researcher will also be responsible for integrating these models with the CWatM hydrological model to support comprehensive and adaptive environmental analyses, ensuring the accurate representation of both groundwater quantity and quality.

Key Responsibilities:

• Hydrological Model Development: Design, develop, and calibrate, transient MODFLOW groundwater models using Python libraries (eg. flopy), ensuring accurate and responsive simulations of Qatar’s aquifer systems.
• Machine Learning Integration: Apply machine learning techniques to enhance the accuracy of the models, and enabling automated updates based on new data inputs.
• Hydrological Model Integration: Collaborate on the integration of these transient groundwater models with the CWatM model, facilitating comprehensive and adaptive analysis of Qatar’s water resources.
• Data Management and Quality Assurance: Manage large-scale, real-time data streams, ensuring data integrity and consistency across all modeling activities.
• Research and Publication: Conduct high-quality research, leading to publications in top-tier journals and presentations at international conferences.
• Collaboration: Work closely with interdisciplinary research teams and external stakeholders, contributing to UDST’s broader research goals in sustainable water management.

Qualifications:

• Ph.D. in Hydrogeology, Hydrology, Environmental Engineering, or a related discipline, with a focus on transient groundwater modeling.
• Technical Skills: Proficiency in Python programming and experience with MODFLOW model development, particularly in creating and calibrating transient models.
• Machine Learning Expertise: Experience in machine learning algorithms application, with a focus on enhancing model accuracy and efficiency.
• Hydrological Modeling: Preferred Knowledge of the CWatm model or similar hydrological models.
• Research Experience: A strong record of research achievements, including publications in peer-reviewed journals and presentations at scientific conferences.
• Communication and Collaboration: Excellent written and oral communication skills, with the ability to work effectively in a multidisciplinary team environment.

Preferable Research Experiences:

• Publication Record: A good history of publications in relevant, high-impact journals.
• Interdisciplinary Collaboration: Experience working within multidisciplinary research teams and contributing to collaborative research projects.
• Data Analysis Proficiency: Strong skills in managing and analyzing transient hydrogeological  data and applying machine learning algorithms.
• Technical Expertise: Demonstrated hands-on experience in laboratory and fieldwork, particularly related to transient groundwater modeling and data collection.

Skills:

The required skills for this position include extensive experience in developing and calibrating transient MODFLOW groundwater models, strong proficiency in Python, and a demonstrated ability to apply machine learning techniques to optimize model performance. The candidate must be skilled in integrating and managing real-time hydrogeological data to ensure accurate, dynamic simulations. Additionally, the candidate should excel in collaborative, multidisciplinary environments, with the ability to communicate complex technical concepts clearly and effectively.

Application procedure

The application should be in English. The application should be sent electronically and be attached as PDF-files, as below. Maximum size for each file is 10 MB.

1. CV:(Please name the document as: CV, Surname, Ref. number) including:

• A complete list of publications
• Previous research experiences
• Two references who can be approached to write letters of reference/recommendation

2. Cover letter: (Please name the document as: Personal letter, Family name, Ref. number) 1-3 pages where you:

• Introduce yourself
• Describe your previous research fields and main research results
• Relate your qualifications, experience, and skills with the job requirements
• Describe your future goals and future research focus