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Study program (academic year 2025/26)*
AI and machine learning for environmental intelligence (2 credits)
AI and machine learning for environmental intelligence (2 credits)
Hands-on AI/ML for Environmental Applications: learn about data mining, ML, and optimization in the context of environmental real-world relevance applications. The course explores in detail how data science techniques can be used to develop solutions to a range of environmental problems. The course covers the basic theory behind machine learning and provides hands-on experience in building machine learning models. We will learn key aspects of data science, including cloud computing, remote sensing, environmental monitoring, and computer code.
Hands-on AI/ML for Environmental Applications: learn about data mining, ML, and optimization in the context of environmental real-world relevance applications. The course explores in detail how data science techniques can be used to develop solutions to a range of environmental problems. The course covers the basic theory behind machine learning and provides hands-on experience in building machine learning models. We will learn key aspects of data science, including cloud computing, remote sensing, environmental monitoring, and computer code.
Computational environmental modeling with MATLAB (2 credits)
Computational environmental modeling with MATLAB (2 credits)
This course focuses on basic programming in the MATLAB development environment, principles, and techniques for developing code and handling spatial information (including multidimensional information). In the first part of the course, we will perform mathematical manipulations on basic functions in linear algebra, then we will focus on spatial data (first a two-dimensional, three-dimensional, and multi-dimensional matrix) and finally, we will learn to build a graphical user interface (GUI). In the second part, we will learn the basics of building algorithms and flow charts, as well as methods for detecting objects in an image.
This course focuses on basic programming in the MATLAB development environment, principles, and techniques for developing code and handling spatial information (including multidimensional information). In the first part of the course, we will perform mathematical manipulations on basic functions in linear algebra, then we will focus on spatial data (first a two-dimensional, three-dimensional, and multi-dimensional matrix) and finally, we will learn to build a graphical user interface (GUI). In the second part, we will learn the basics of building algorithms and flow charts, as well as methods for detecting objects in an image.
Digital image processing (elective course; 2 credits)**
Digital image processing (elective course; 2 credits)**
The course offers fundamental tools in digital image processing, with a focus on task-oriented theory and algorithms. It covers data acquisition, enhancement, segmentation, and representation, emphasizing both mathematical understanding and practical implementation. It provides an overview of the field, including visual perception, image formation (radiometry), spatial transformations (geometry), image enhancement, color image representation and design, image segmentation, and morphological processing. Additionally, the course introduces 3D image reconstruction and interpretation.
The course offers fundamental tools in digital image processing, with a focus on task-oriented theory and algorithms. It covers data acquisition, enhancement, segmentation, and representation, emphasizing both mathematical understanding and practical implementation. It provides an overview of the field, including visual perception, image formation (radiometry), spatial transformations (geometry), image enhancement, color image representation and design, image segmentation, and morphological processing. Additionally, the course introduces 3D image reconstruction and interpretation.
Environmental economics (4 credits)
Environmental economics (4 credits)
The goal of the course is to provide students with the theoretical and empirical tools required to understand and apply the concepts of public goods, external costs, non-market valuation of environmental resources, cost-benefit analysis, and economic policy instruments such as environmental taxes and cap-and-trade schemes. Specific instruments for the monetary valuation of ecosystem goods and services that are not the object of market transactions are presented. The course combines lectures with practical exercise sessions.
The goal of the course is to provide students with the theoretical and empirical tools required to understand and apply the concepts of public goods, external costs, non-market valuation of environmental resources, cost-benefit analysis, and economic policy instruments such as environmental taxes and cap-and-trade schemes. Specific instruments for the monetary valuation of ecosystem goods and services that are not the object of market transactions are presented. The course combines lectures with practical exercise sessions.
GIS for natural resources management (3 credits)
GIS for natural resources management (3 credits)
The course equips students with practical skills to analyze spatial data for environmental and resource management. It covers an introduction to GIS-Pro software, along with key methodologies for working with spatial data. During the semester, students will exercise with various tools to enhance their skills. Additionally, students will learn how to search for and integrate external data and layers. By the end of the semester, students will be able to create and edit maps, perform data manipulation and calculations, and utilize basic GIS-Pro features.
The course equips students with practical skills to analyze spatial data for environmental and resource management. It covers an introduction to GIS-Pro software, along with key methodologies for working with spatial data. During the semester, students will exercise with various tools to enhance their skills. Additionally, students will learn how to search for and integrate external data and layers. By the end of the semester, students will be able to create and edit maps, perform data manipulation and calculations, and utilize basic GIS-Pro features.
Advanced spatial data analysis (4 credits)
Advanced spatial data analysis (4 credits)
This course provides a comprehensive introduction to ArcGIS Pro, covering vector- and raster-based analyses, spatial modeling, and GIS solutions across desktop, web, mobile, and server platforms. Students will explore enterprise GIS architecture and practice creating spatial models. Course objectives include mastering core ArcGIS functionalities and applying GIS techniques in a final project. Requirements include attendance, camera-enabled participation in lessons, submission of all exercises, and a final project. Topics range from georeferencing and spatial modeling to 3D analysis, hydrology, and enterprise GIS solutions.
This course provides a comprehensive introduction to ArcGIS Pro, covering vector- and raster-based analyses, spatial modeling, and GIS solutions across desktop, web, mobile, and server platforms. Students will explore enterprise GIS architecture and practice creating spatial models. Course objectives include mastering core ArcGIS functionalities and applying GIS techniques in a final project. Requirements include attendance, camera-enabled participation in lessons, submission of all exercises, and a final project. Topics range from georeferencing and spatial modeling to 3D analysis, hydrology, and enterprise GIS solutions.
Digital footprints for environmental intelligence (2 credits)
Digital footprints for environmental intelligence (2 credits)
The primary objective of this course is to provide students with an in-depth exploration of the use of digital footprints in environmental research. Digital footprints are the digital data and information that are left behind by a person’s online activities. The course will cover the different types of digital footprints data, including user-generated content, social media data, and Volunteered Geographic Information. Students will also learn about the objectives, methods, and tools that are most relevant in the environmental sciences. The focus is on practical application of protocols for data retrieval, management and analysis.
The primary objective of this course is to provide students with an in-depth exploration of the use of digital footprints in environmental research. Digital footprints are the digital data and information that are left behind by a person’s online activities. The course will cover the different types of digital footprints data, including user-generated content, social media data, and Volunteered Geographic Information. Students will also learn about the objectives, methods, and tools that are most relevant in the environmental sciences. The focus is on practical application of protocols for data retrieval, management and analysis.
Ecosystem services (elective course; 2 credits)**
Ecosystem services (elective course; 2 credits)**
The ecosystem service framework is recognized as a cornerstone of sustainable environmental management. A number of tools and software applications have been developed in recent years that can be used to assess, quantify, map and value ecosystem services. This course first introduces the concepts and taxonomy of the ecosystem service framework and the role it can play in the promotion of sustainable management. The core of the course consists in providing the students with hands-on experience with ecosystem service assessment tools such as InVEST, and applications to the assessment of a range of cultural, regulating and provisioning services.
The ecosystem service framework is recognized as a cornerstone of sustainable environmental management. A number of tools and software applications have been developed in recent years that can be used to assess, quantify, map and value ecosystem services. This course first introduces the concepts and taxonomy of the ecosystem service framework and the role it can play in the promotion of sustainable management. The core of the course consists in providing the students with hands-on experience with ecosystem service assessment tools such as InVEST, and applications to the assessment of a range of cultural, regulating and provisioning services.
Environmental computational science and Earth observation (2 credits)
Environmental computational science and Earth observation (2 credits)
The course provides hands-on experience with remote sensing systems and applications, focusing on principles for developing algorithms to interpret hyperspectral imagery and skills in machine learning (ML) tailored to specific remote sensing challenges and data from various sensors. Key topics include anomaly detection, sub-pixel unmixing, change detection monitoring, and classification and segmentation using ML.
The course provides hands-on experience with remote sensing systems and applications, focusing on principles for developing algorithms to interpret hyperspectral imagery and skills in machine learning (ML) tailored to specific remote sensing challenges and data from various sensors. Key topics include anomaly detection, sub-pixel unmixing, change detection monitoring, and classification and segmentation using ML.
Environmental modelling (elective course; 2 credits)**
Environmental modelling (elective course; 2 credits)**
Models are essential tools for better understanding environmental processes, planning, and forecasting future scenarios influenced by factors like climate change and direct human-related stressors. During the course, students will gain an understanding of computational models in general, as well as their specific applications in environmental sciences. They will become familiar with tools for building and implementing models, focusing on sensitivity and error analysis. Course topics include model structure, model design, types of models (empirical/physical), development and application environments for models, boundary conditions, sensitivity tests, calibration, validation, and error assessment.
Models are essential tools for better understanding environmental processes, planning, and forecasting future scenarios influenced by factors like climate change and direct human-related stressors. During the course, students will gain an understanding of computational models in general, as well as their specific applications in environmental sciences. They will become familiar with tools for building and implementing models, focusing on sensitivity and error analysis. Course topics include model structure, model design, types of models (empirical/physical), development and application environments for models, boundary conditions, sensitivity tests, calibration, validation, and error assessment.
Generative AI in environmental informatics (2 credits)
Generative AI in environmental informatics (2 credits)
This course explores the application of Generative AI (GenAI) tools and methods to challenges in environmental informatics, including data augmentation, modeling, prediction, visualization, and decision support. Through a collection of case studies and datasets, students will gain hands-on experience with GenAI models for addressing real-world environmental problems. The course is intended for students with a background in environmental science, engineering, or data science who are interested in harnessing the power of generative models to drive innovation in environmental analysis and sustainability.
This course explores the application of Generative AI (GenAI) tools and methods to challenges in environmental informatics, including data augmentation, modeling, prediction, visualization, and decision support. Through a collection of case studies and datasets, students will gain hands-on experience with GenAI models for addressing real-world environmental problems. The course is intended for students with a background in environmental science, engineering, or data science who are interested in harnessing the power of generative models to drive innovation in environmental analysis and sustainability.
Quantitative research methods (2 credits)
Quantitative research methods (2 credits)
The goal of this online course is for students to get to know and use advanced quantitative data analysis methods. The course will focus on the application of these methods but will also ensure students understand the rationale for the different methods and when it is suitable to apply them. We will place less emphasis on the mathematics behind the methods and more on the broad idea the analysis is capturing and on the applicability of these methods using SPSS or other statistical analysis tools.
The goal of this online course is for students to get to know and use advanced quantitative data analysis methods. The course will focus on the application of these methods but will also ensure students understand the rationale for the different methods and when it is suitable to apply them. We will place less emphasis on the mathematics behind the methods and more on the broad idea the analysis is capturing and on the applicability of these methods using SPSS or other statistical analysis tools.
Smart environmental systems (2 credits)
Smart environmental systems (2 credits)
This course introduces mathematical programming and modeling approaches for natural resources and environmental management. Optimization methods and game theory concepts will be applied to water resources and environmental systems management, resource conservation, and pollution control. By the end of the course, students will be able to apply the tools to optimize systems for a set of constraints and objectives and perform sensitivity analysis of decisions. The course will cover concepts of tradeoffs, non-inferior sets, and multi-objective optimization concepts. The course will also include exercises via Excel and MATLAB.
This course introduces mathematical programming and modeling approaches for natural resources and environmental management. Optimization methods and game theory concepts will be applied to water resources and environmental systems management, resource conservation, and pollution control. By the end of the course, students will be able to apply the tools to optimize systems for a set of constraints and objectives and perform sensitivity analysis of decisions. The course will cover concepts of tradeoffs, non-inferior sets, and multi-objective optimization concepts. The course will also include exercises via Excel and MATLAB.
Strategic approaches to contemporary environmental challenges (4 credits)
Strategic approaches to contemporary environmental challenges (4 credits)
This course examines multifaceted environmental challenges to first establish a systematic and comprehensive understanding and then develop strategic approaches to address them. Topics include terrestrial and marine pollution, energy sources, non-renewable material resources, marine chokepoints, supply chains, water resources, geopolitics, environmental tipping points, and extreme events. Case studies will cover topics from around the world. Course objectives include understanding environmental strategies through global processes and case studies and developing analytical skills to evaluate environmental challenges, assess strategic options, and propose evidence-based solutions for complex environmental issues.
This course examines multifaceted environmental challenges to first establish a systematic and comprehensive understanding and then develop strategic approaches to address them. Topics include terrestrial and marine pollution, energy sources, non-renewable material resources, marine chokepoints, supply chains, water resources, geopolitics, environmental tipping points, and extreme events. Case studies will cover topics from around the world. Course objectives include understanding environmental strategies through global processes and case studies and developing analytical skills to evaluate environmental challenges, assess strategic options, and propose evidence-based solutions for complex environmental issues.
Industry-academy colloquium (2 credits)
Industry-academy colloquium (2 credits)
Students will engage with a diverse range of real-world environmental management applications presented by professionals from various fields, within a seminar-style format. This course aims to showcase the practical implementation of environmental intelligence research.
Students will engage with a diverse range of real-world environmental management applications presented by professionals from various fields, within a seminar-style format. This course aims to showcase the practical implementation of environmental intelligence research.
Capstone project (4 credits)***
Capstone project (4 credits)***
The objective of the capstone project is to apply and integrate the knowledge and skills acquired throughout the program to address a real-world problem or research question, demonstrating a comprehensive understanding of the subject matter and the ability to produce valuable, practical outcomes. Projects must involve a problem-solving approach to the analysis and development of solutions for specific environmental, economic or social sustainability issues, not necessarily research oriented. Students typically work in groups of two.
The objective of the capstone project is to apply and integrate the knowledge and skills acquired throughout the program to address a real-world problem or research question, demonstrating a comprehensive understanding of the subject matter and the ability to produce valuable, practical outcomes. Projects must involve a problem-solving approach to the analysis and development of solutions for specific environmental, economic or social sustainability issues, not necessarily research oriented. Students typically work in groups of two.
To complete the program, students must obtain a minimum of 36 academic credits.
To complete the program, students must obtain a minimum of 36 academic credits.
Students in the thesis track need to complete a thesis during the second year of the program (8 credits).
* The curriculum may be subject to change.
** Students must choose at least one out of all elective courses.
*** Mandatory for project track only.
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