Train to shape the ecosystems of tomorrow
Knowing whether an ecosystem is in decline, and in urgent need of intervention to turn around loss of biodiversity, is crucial to managers and policy makers. Generating that knowledge requires collecting data in the field, analyzing it with advanced computational methods, and communicating the results effectively to those in the position to act on them. That is the role of ecological forecasters.
Forecasting is not a purely technical exercise, but a process that supports real-world decisions. That means students need to learn not only to build and evaluate models, but to understand how uncertainty is communicated to and interpreted by those who act on forecasts. To that end, students will develop expertise in field-based data collection across different ecological systems, data analysis with statistical models and deep learning, and decision theory.
Profile
The Master’s program in Ecological Forecasting is part of the Elite Graduate Programs of the Elite Network of Bavaria. The program is built around four distinct stages: foundations, themes, pilot study and forecasting challenge, and a master thesis. Each stage builds the skills students need to work as ecological forecasters in research, policy, or management.
The essentials at a glance
Faculty: Faculty of Biology, Chemistry & Earth Sciences
Final degree: Master of science (M.Sc.)
Start of studies: Winter semester
Standard period of study: 4 semesters
Language of instruction: English
Admission requirements: Language proficiency: English level B2, German level A1 (can be acquired by the end of the second semester)
Contact
Programme coordination
Prof. Dr. Steven Higgins
E-Mail: mef@uni-bayreuth.de
Student Advising (in German language)
Tel.: 0921/55-4433
E-Mail: studienberatung@uni-bayreuth.de
Structure and content
The program draws on content from ecology, ecological modelling, statistics, artificial intelligence, remote sensing, philosophy, and economics. Teaching modules are designed to build upon one another in a structured and coordinated manner. The Foundations in Ecological Forecasting modules introduce core concepts that are reinforced and expanded in the subsequent Themes in Ecological Forecasting modules, ensuring a cohesive learning experience and a gradual progression of skills and knowledge.
Foundations in Ecological Forecasting (40 ECTS)
The compulsory Foundations in Ecological Forecasting modules introduce the core concepts required by all ecological forecasters. Modules cover the foundations of ecology, scientific programming, probability theory, deep learning, statistical modelling, remote sensing, and the philosophical and behavioural-economic aspects of decision-making.
Themes in Ecological Forecasting (20–30 ECTS)
Concepts introduced in the Foundations in Ecological Forecasting modules are reinforced and expanded in the Themes in Ecological Forecasting modules. These modules allow students to apply foundational methods to develop forecasts relevant to forest, open-ecosystem, aquatic, and systems ecology, as well as to evolutionary and conservation biology. Students also apply forecasting approaches in the context of developing innovation projects for sustainability.
Pilot Study (5–15 ECTS)
In the Pilot Study modules, students individually undertake internships in which they acquire specialised methods and gain exposure to specific forecasting domains. Pilot Study internships may be completed with external regional, national, or international organisations, or within the research group of a faculty member.
Forecasting Challenge (5 ECTS)
The Forecasting Challenge module provides students with hands-on experience in collaborative and interdisciplinary research focused on producing clearly defined ecological forecasts.
Research Support (10 ECTS)
The Research Proposal and Peer Review modules (5 ECTS each) support students in planning their Master’s project while receiving structured feedback from their peers. An iterative review process allows for multiple rounds of feedback and improvement. Embedded in a research-based teaching framework, these modules emulate the peer-review process that is central to scientific practice.
Master’s Project (30 ECTS)
The Master’s project is conducted under the supervision of a faculty member from the program. It overlaps in time with the Peer Review module, allowing peer feedback to be directly incorporated and used to improve the quality of the submitted Master’s thesis.
Contact
Programme coordination
Prof. Dr. Steven Higgins
E-Mail: mef@uni-bayreuth.de
Student Advising (in German language)
Tel.: 0921/55-4433
E-Mail: studienberatung@uni-bayreuth.de
Career prospects
Graduates can pursue careers either as forecast makers —engaged in fundamental and applied research in ecological forecasting— or as forecast interpreters, applying ecological forecasts in decision-making, ecosystem management, and policy contexts. The program prepares graduates for careers in a wide range of sectors that rely on ecological forecasts, including:
- Ecological and environmental research
- Governmental agencies and environmental authorities
- Non-governmental organizations (NGOs) focused on biodiversity, conservation, or global change
- Environmental and sustainability consulting
- Forestry, agriculture, and landscape planning
- Industry sectors such as insurance, renewable energy, and environmental risk analysis
- Data science and environmental informatics
Contact
Programme coordination
Prof. Dr. Steven Higgins
E-Mail: mef@uni-bayreuth.de
Student Advising (in German language)
Tel.: 0921/55-4433
E-Mail: studienberatung@uni-bayreuth.de
Notes on application and enrolment
Application period
- for German and EU citizens:
1 March to 15 July for winter semester - for non EU citizens:
1 March to 15 July for winter semester
Application guide
Individual information on the application process (documents, deadlines, link to the application portal)
click here for German version
Admission requirements
Qualification
A university degree (or completed course of study) in one of the following Bachelor’s programs: Biology, Geoecology, Environmental Sciences, Philosophy and Economics, Physics, Computational Mathematics, Mathematics, Applied Informatics, Data Science and Artificial Intelligence, or in a closely related discipline.
Aptitude Assessment Process
One prerequisite for admission to the program is the successful completion of an aptitude assessment process (see Examination Regulations, Annex 2). The process is conducted in English and consists of two stages: Stage I, an evaluation of the applicant’s academic qualifications, and Stage II, a personal interview.
Language proficiency
ENGLISH: level B2
GERMAN: level A1 (until the end of 2nd semester)
Applying without a degree certificate
Applications may already be submitted before completion of the bachelor's degree; however, a total of at least 135 credit points must be earned by the time of application.
Contact
Programme coordination
Prof. Dr. Steven Higgins
E-Mail: mef@uni-bayreuth.de
Student Advising (in German language)
Tel.: 0921/55-4433
E-Mail: studienberatung@uni-bayreuth.de
) anklickst; Du findest es bei jedem Studiengang. Deine Auswahl siehst Du, wenn Du am oberen Seitenrand auf „Favoriten“ klickst.
) klickst. Dann bekommst Du per Mail einen Link, über den Du Deine Favoriten wieder abrufen und weiter bearbeiten kannst. Sobald Du Deine Favoriten einmal gespeichert hast, werden alle Änderungen, die Du vornimmst, automatisch übernommen. Deine Favoriten bleiben weiterhin über den Link in der Mail abrufbar.