EN
Department: School of Agriculture and Food Sciences
Brief information about the School of Agriculture and Food Sciences at ADA University
More information about SAFS can be found at https://www.ada.edu.az/en/schools/safs
The Master in Agricultural and Food System Management
The Master's Degree Course in Agricultural and Food Systems Management is a 2-year Cycle Degree Programme designed with an interdisciplinary approach and an international outlook. It is a multidisciplinary programme pivoted on agricultural and food economics, providing a combination of analytical and quantitative skills with an emphasis on management, policy, and practices in the agri-food sector. The programme builds on the connections between different fields of knowledge focused on agricultural and food systems sustainability in order to train prospective decision-makers in facing real management problems in international contexts and diverse organizational settings.
The programme aims to provide students with a broad perspective on the complexity of economic and organizational aspects related to agricultural and food value chains with particular attention to environmental and human sustainability issues. By developing skills related to sustainable entrepreneurship management, agri-food business planning and venturing, the course allows students to acquire knowledge, as well as the logic, methodologies and tools to undertake a managerial career in industrial and commercial enterprises, in public organisations, in service and consultancy companies, in research centres, developing entrepreneurial aptitudes towards innovative start-ups and enterprises within the agri-food sector.
The qualifying feature is the advanced level of the study course with particular attention to the interdisciplinary nature of economics and business. The course aims to provide students with an innovative, multidisciplinary and systemic perspective with the objective of building their careers in companies and organizations where they can broad the vision and build a sustainable future.
Students will acquire the essential knowledge and tools to undertake a fulfilling managerial career in the agri-food industry.
More information about the Master in AFSM can be found at https://www.ada.edu.az/en/schools/safs/programs/agricultural-and-food-system-management
Brief description of responsibilities:
Principal responsibilities include teaching a graduate course in Data Mining. We are seeking dynamic and energetic scholars who have a desire to work collaboratively in an international academic environment.
Basic knowledge required to teach the course contents:
- Demonstrated knowledge and ability to teach students how to adopt specific data analysis techniques to different typologies of data for the analysis and the interpretation of the phenomena characterizing the food systems and for the evaluation of policies, both from micro and macro perspectives. Data analysis techniques should be referred to cross-section data and time series.
- Demonstrated knowledge and ability to teach students to use software as R, Stata, Excel to analyze and report data from the food sector
- Capacity to understand students’ needs and expectations
- Capacity to stimulate and inspire students
Qualifications:
- Candidates for Instructor position must have a PhD in Economics, Statistics, Political Sciences, or other comparable topics.
- Experience in designing and teaching courses in the field of Economics and Food Economics
- Proven experiences in dealing with real cases related to agriculture and food with particular emphasis in agricultural and food economics
- Proven international background and ability to work in multicultural contexts
- Proficiency in spoken and written English
- At least 2 years of teaching experience at an accredited institution
Women and members of minority groups are strongly urged to apply.
Send letter of application (max 1 page), curriculum vitae, portfolio of selected students’ works (if available), list of publications and link/PDF to maximum three papers, summary of teaching evaluations (if available), and contact information of three references to afsrecruitment@ada.edu.az
The cut-off date for application is January 10th, 2025.
Details of the job position
Programme: Master in Agricultural and Food Sciences
Year: 1st
Disciplinary Area: Agricultural and Food Economics and Policy
Course (tbc): Data mining
Semester: Second
Start date: February 2025 (teaching starts March/April 2025)
Description of the Disciplinary Area
This core subject area develops students' theoretical and practical expertise in statistical and econometric techniques in the field of knowledge discovery in business databases for the analysis of the dynamics of food systems at both micro and macro levels. It introduces students to applied methods for planning a data collection process and for analyzing cross-sectional and time series data
Through theoretical lectures and practical exercises with simulated and real-world datasets, including those from public databases like WTO Statistics Database, FAOSTAT, World Bank Databases, EUROSTAT, National databases, students gain practical experience in applying these techniques using software tools such as Excel, R, and STATA. Additionally, the course emphasizes the importance of data visualization and reporting, providing students the skills needed to effectively organize, process, and communicate results.
General Objectives of the Course
The data mining course provides students with fundamental knowledge of statistical methods for data analysis for applications in the food sector and the analysis of its dynamics and policies. It builds on a basic understanding of statistical and econometric concepts, including means, variances, regressions, and p-values, as well as basic proficiency in Excel and R software and a general familiarity with the food sector.
The course enables students to identify and apply the most appropriate descriptive and inferential methodologies for the analysis of the dynamics of food systems, both at the micro level, such as consumer behavior and producer decision-making, and at the macro level, such as the analysis of commodity price trends.
The course combines theoretical lectures with practical sessions, using real-world data from international databases, such as WTO Statistics Database, FAOSTAT, World Bank Databases, EUROSTAT, National databases to illustrate case studies. Students will be introduced to advanced methods for analyzing experimental and quasi-experimental data, such as Randomized Control Trials, Choice Experiments, Difference-in-Differences, and Regression Discontinuity Design. Techniques for cross-sectional data analysis, including clustering and Principal Component Analysis, and for time series analysis, such as auto-regressive models like ARMA and ARIMA, will also be covered, with a specific focus on the dynamics of food systems. The course places significant emphasis on data visualization and reporting, equipping students with the skills to create clear and effective reports that communicate analytical results comprehensively.
