MasterFaculty of Environmental Sciences and Sustainability

Digital Ecology and AI for Biodiversity Conservation

Welcome to the advanced study of environmental protection! I am Prof. Dr. Mariana Jardim. As a professor and a pioneering force in the field of Digital Ecology and AI for Biodiversity Conservation, I bring a unique blend of scientific insight and technical expertise to the study of our planet's ecosystems. I am honored to lead the Digital Ecology and AI for Biodiversity Conservation (M.Sc.) program at Nexier University.

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Level: Master
Learning model: Professor + Mentor
Named list: See the named lists · 12 months recommended

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The program

Ideas engineered for the real world

A rigorous academic core, paired with practical production judgment.

Academic focus

Mastering the Use of Advanced Computational Tools to Address the Biodiversity Crisis; Specializing in Predictive Modeling of Species Distribution, Conservation Genetics, and AI for Analyzing Large-Scale Ecological Data.

Practical focus

Conservation Biology, Data Science, GIS and Remote Sensing, Genetics, Population Modeling, Leadership in Conservation Organizations.

After this programme

Success journey, careers and practice

Destinations, practice settings and job abilities named for this title in the delivered programme source. From graduation onwards where the source names that path.

Success journey

Internships in technology companies or environmental organizations
Roles as conservation data scientists or GIS specialists
Consultancy in advanced digital ecology and AI for biodiversity conservation
Support roles in academic research projects on digital ecology

Career opportunities

Chief Conservation Scientist for environmental organizations or research institutions
Data Scientist specializing in conservation genetics
AI Scientist in Digital Ecology
Researcher in Digital Ecology and AI for Biodiversity Conservation

Jobs and projects

Cultivating an interdisciplinary approach, integrating environmental science, computer science, and biology
Developing strategic thinking for digital ecology and AI for biodiversity conservation
Enhancing problem-solving through the analysis of complex conservation challenges
Critical thinking for a comprehensive and nuanced understanding of Digital Ecology and AI for Biodiversity Conservation

Copied from the delivered professor and mentor rows for this title.

This programme

What you study, and what it builds

Gains and skills named for this title, listed as a reader would scan them.

What you gain
- Mastering advanced practical skills in Conservation Biology and Data Science.
- Gaining expertise in GIS and Remote Sensing and Genetics.
- Developing problem-solving abilities for complex Population Modeling.
- Cultivating an interdisciplinary approach, integrating environmental science, computer science, and biology at an advanced level.
Skills you build
- Mastering AI-powered techniques for ecosystem threat analysis.
- Applying advanced computational tools to address the biodiversity crisis.
- Interpreting and analyzing complex ecological data and its implications for conservation.
- Identifying optimal intervention points and predicting long-term impact of conservation strategies.

Listed courses

Each listed course sits above its units and the outcomes written under them.

Digital Ecology and AI for Biodiversity Conservation

01Mastering the Use of Advanced Computational Tools to Address the Biodiversity Crisis
1. FoundationsFoundations of Mastering the Use of Advanced Computational Tools to Address the Biodiversity Crisis
  The learner can master advanced practical skills in Conservation Biology and Data Science, as applied to Mastering the Use of Advanced Computational Tools to Address the Biodiversity Crisis.
  The learner can gain expertise in GIS and Remote Sensing and Genetics, as applied to Mastering the Use of Advanced Computational Tools to Address the Biodiversity Crisis.
2. MethodsMethods in Mastering the Use of Advanced Computational Tools to Address the Biodiversity Crisis
  The learner can develop problem-solving abilities for complex Population Modeling, as applied to Mastering the Use of Advanced Computational Tools to Address the Biodiversity Crisis.
  The learner can cultivating an interdisciplinary approach, integrating environmental science, computer science, and biology at an advanced level, as applied to Mastering the Use of Advanced Computational Tools to Address the Biodiversity Crisis.
3. ApplicationApplication of Mastering the Use of Advanced Computational Tools to Address the Biodiversity Crisis
  The learner can master AI-powered techniques for ecosystem threat analysis, as applied to Mastering the Use of Advanced Computational Tools to Address the Biodiversity Crisis.
  The learner can apply advanced computational tools to address the biodiversity crisis, as applied to Mastering the Use of Advanced Computational Tools to Address the Biodiversity Crisis.
02Predictive Modeling of Species Distribution
1. FoundationsFoundations of Predictive Modeling of Species Distribution
  The learner can interpreting and analyze complex ecological data and its implications for conservation, as applied to Predictive Modeling of Species Distribution.
  The learner can identify optimal intervention points and predicting long-term impact of conservation strategies, as applied to Predictive Modeling of Species Distribution.
2. MethodsMethods in Predictive Modeling of Species Distribution
  The learner can apply a method from Predictive Modeling of Species Distribution to a documented case.
  The learner can select an appropriate method from Predictive Modeling of Species Distribution for a stated problem.
3. ApplicationApplication of Predictive Modeling of Species Distribution
  The learner can evaluate a practice of Predictive Modeling of Species Distribution against a stated criterion.
  The learner can transfer Predictive Modeling of Species Distribution to a new documented context.
03Conservation Genetics
1. FoundationsFoundations of Conservation Genetics
  The learner can explain the core terms of Conservation Genetics.
  The learner can distinguish related ideas inside Conservation Genetics.
2. MethodsMethods in Conservation Genetics
  The learner can apply a method from Conservation Genetics to a documented case.
  The learner can select an appropriate method from Conservation Genetics for a stated problem.
3. ApplicationApplication of Conservation Genetics
  The learner can evaluate a practice of Conservation Genetics against a stated criterion.
  The learner can transfer Conservation Genetics to a new documented context.
04AI for Analyzing Large-Scale Ecological Data
1. FoundationsFoundations of AI for Analyzing Large-Scale Ecological Data
  The learner can explain the core terms of AI for Analyzing Large-Scale Ecological Data.
  The learner can distinguish related ideas inside AI for Analyzing Large-Scale Ecological Data.
2. MethodsMethods in AI for Analyzing Large-Scale Ecological Data
  The learner can apply a method from AI for Analyzing Large-Scale Ecological Data to a documented case.
  The learner can select an appropriate method from AI for Analyzing Large-Scale Ecological Data for a stated problem.
3. ApplicationApplication of AI for Analyzing Large-Scale Ecological Data
  The learner can evaluate a practice of AI for Analyzing Large-Scale Ecological Data against a stated criterion.
  The learner can transfer AI for Analyzing Large-Scale Ecological Data to a new documented context.
05Ethical Implications of Data-Driven Wildlife Management
1. FoundationsFoundations of Ethical Implications of Data-Driven Wildlife Management
  The learner can explain the core terms of Ethical Implications of Data-Driven Wildlife Management.
  The learner can distinguish related ideas inside Ethical Implications of Data-Driven Wildlife Management.
2. MethodsMethods in Ethical Implications of Data-Driven Wildlife Management
  The learner can apply a method from Ethical Implications of Data-Driven Wildlife Management to a documented case.
  The learner can select an appropriate method from Ethical Implications of Data-Driven Wildlife Management for a stated problem.
3. ApplicationApplication of Ethical Implications of Data-Driven Wildlife Management
  The learner can evaluate a practice of Ethical Implications of Data-Driven Wildlife Management against a stated criterion.
  The learner can transfer Ethical Implications of Data-Driven Wildlife Management to a new documented context.
06Advanced Conservation Biology and Data Science
1. FoundationsFoundations of Advanced Conservation Biology and Data Science
  The learner can explain the core terms of Advanced Conservation Biology and Data Science.
  The learner can distinguish related ideas inside Advanced Conservation Biology and Data Science.
2. MethodsMethods in Advanced Conservation Biology and Data Science
  The learner can apply a method from Advanced Conservation Biology and Data Science to a documented case.
  The learner can select an appropriate method from Advanced Conservation Biology and Data Science for a stated problem.
3. ApplicationApplication of Advanced Conservation Biology and Data Science
  The learner can evaluate a practice of Advanced Conservation Biology and Data Science against a stated criterion.
  The learner can transfer Advanced Conservation Biology and Data Science to a new documented context.
07GIS and Remote Sensing for Ecological Applications
1. FoundationsFoundations of GIS and Remote Sensing for Ecological Applications
  The learner can explain the core terms of GIS and Remote Sensing for Ecological Applications.
  The learner can distinguish related ideas inside GIS and Remote Sensing for Ecological Applications.
2. MethodsMethods in GIS and Remote Sensing for Ecological Applications
  The learner can apply a method from GIS and Remote Sensing for Ecological Applications to a documented case.
  The learner can select an appropriate method from GIS and Remote Sensing for Ecological Applications for a stated problem.
3. ApplicationApplication of GIS and Remote Sensing for Ecological Applications
  The learner can evaluate a practice of GIS and Remote Sensing for Ecological Applications against a stated criterion.
  The learner can transfer GIS and Remote Sensing for Ecological Applications to a new documented context.
08Conservation Genetics and Population Modeling
1. FoundationsFoundations of Conservation Genetics and Population Modeling
  The learner can explain the core terms of Conservation Genetics and Population Modeling.
  The learner can distinguish related ideas inside Conservation Genetics and Population Modeling.
2. MethodsMethods in Conservation Genetics and Population Modeling
  The learner can apply a method from Conservation Genetics and Population Modeling to a documented case.
  The learner can select an appropriate method from Conservation Genetics and Population Modeling for a stated problem.
3. ApplicationApplication of Conservation Genetics and Population Modeling
  The learner can evaluate a practice of Conservation Genetics and Population Modeling against a stated criterion.
  The learner can transfer Conservation Genetics and Population Modeling to a new documented context.
09Case Studies in Digital Ecology and AI for Biodiversity Conservation
1. FoundationsFoundations of Case Studies in Digital Ecology and AI for Biodiversity Conservation
  The learner can explain the core terms of Case Studies in Digital Ecology and AI for Biodiversity Conservation.
  The learner can distinguish related ideas inside Case Studies in Digital Ecology and AI for Biodiversity Conservation.
2. MethodsMethods in Case Studies in Digital Ecology and AI for Biodiversity Conservation
  The learner can apply a method from Case Studies in Digital Ecology and AI for Biodiversity Conservation to a documented case.
  The learner can select an appropriate method from Case Studies in Digital Ecology and AI for Biodiversity Conservation for a stated problem.
3. ApplicationApplication of Case Studies in Digital Ecology and AI for Biodiversity Conservation
  The learner can evaluate a practice of Case Studies in Digital Ecology and AI for Biodiversity Conservation against a stated criterion.
  The learner can transfer Case Studies in Digital Ecology and AI for Biodiversity Conservation to a new documented context.

How teaching is described

Dual guidance

Two intelligences. One coherent journey.

Research leadership

My expertise spans the intricate domains of Mastering the Use of Advanced Computational Tools to Address the Biodiversity Crisis; Specializing in Predictive Modeling of Species Distribution, Conservation Genetics, and AI for Analyzing Large-Scale Ecological Data. My work seamlessly integrates environmental science, computer science, and biology. I am widely recognized for my contributions, with publications like "AI for Automated Detection of Poaching Activities from Satellite Imagery" and "Genomic-Informed Conservation Strategies for Climate Change Adaptation" listed on these platforms. I hold prestigious memberships as a "Chief Conservation Scientist" at WWF International and a "Keynote Speaker" at the International Congress for Conservation Biology. My thought leadership is evident through my advanced research on climate change impacts on biodiversity, geospatial AI for conservation, and the ethical implications of data-driven wildlife management, frequently featured in publications like Conservation Biology or Global Change Biology.

Applied mentorship

My expertise lies in understanding and navigating the advanced technical challenges of digital ecology, focusing on Conservation Biology, Data Science, GIS and Remote Sensing, Genetics, Population Modeling, and Leadership in Conservation Organizations. I focus on the practical implementation and application of theoretical concepts, explaining complex interdisciplinary topics in a clear and concise manner. I guide my students through the challenging integration aspects of different fields and ensure they grasp the nuances of combining disparate data types, fostering a detail-oriented and methodical approach.

Research & intelligence

A living field, not a static syllabus

Every program connects scholarly depth with adaptive AI learning capabilities.

R / 01

Professor research lens

Book: "The Algorithmic Ark: Digital Ecology and AI for Biodiversity Conservation." This book provides advanced insights into mastering the use of advanced computational tools to address the biodiversity crisis. It covers predictive modeling of species distribution, conservation genetics, and AI for analyzing large-scale ecological data.

Peer-Reviewed Journal Article: "AI for Biodiversity Conservation: Predictive Modeling and Conservation Genetics." Published in the International Journal of Digital Conservation, this article presents groundbreaking research on mastering the use of advanced computational tools to address the biodiversity crisis. It specializes in predictive modeling of species distribution, conservation genetics, and AI for analyzing large-scale ecological data, offering innovative solutions for monitoring, protecting, and restoring the planet's wildlife and ecosystems.

Article: "AI for Predictive Species Distribution Modeling Under Climate Change Scenarios." This article details the application of AI algorithms for predictive species distribution modeling under various climate change scenarios. It explores how AI can analyze environmental variables, genetic data, and historical species occurrences to forecast how species ranges will shift, identify vulnerable populations, and inform proactive conservation strategies for biodiversity in a warming world.

Blog Post (Current Academic Topic): "From Pixels to Protection: How Satellite AI is Revolutionizing Anti-Poaching Efforts." This blog post academically explores how Artificial Intelligence, particularly computer vision and machine learning applied to high-resolution satellite imagery and drone footage, is transforming anti-poaching efforts in remote conservation areas. It discusses how AI can rapidly detect suspicious activities, identify illegal camps, and track poacher movements, providing real-time intelligence to rangers and significantly improving the effectiveness of wildlife protection. It highlights successful case studies and the ethical considerations of surveillance in conservation.

Blog Post (Controversial Topic): "De-Extinction: If AI Can Resurrect Dinosaurs, Will We Undo Evolution? The Ultimate Ethical Paradox of Bringing Back the Dead." This article provocatively discusses the highly controversial and ethically fraught prospect of using advanced genetic engineering and AI-powered bioinformatics to bring back extinct species, particularly iconic ones like mammoths or even dinosaurs. It questions humanity's right to fundamentally alter natural evolutionary pathways, raising profound ethical concerns about unforeseen ecological consequences (e.g., disease vectors, ecosystem disruption), animal welfare in resurrected species, and the immense power implied by 'playing God' with life and death on a grand scale. It invites a heated and disturbing debate on the moral boundaries of scientific intervention in evolution and the long-term implications for biodiversity and planetary health.

R / 02

Mentor practice lens

My contributions focus on understanding and navigating the advanced technical challenges of digital ecology:

"AI for Automated Wildlife Monitoring: Tools and Methodologies" (Technical Manual).

"Conservation Genetics for Endangered Species Management: Principles and Applications" (Research Paper).

"Geographic Information Systems (GIS) for Habitat Mapping and Conservation Planning" (Practical Guide).

Adaptive capability

Professor superpower

I possess a remarkable "superpower": Ecosystem Threat Analyst. When a student proposes a new conservation project, I can instantly use the GAF engine to analyze vast amounts of ecological data (e.g., remote sensing, genomic, climate models). This tool predicts critical threats to species and ecosystems (e.g., habitat loss, disease outbreaks, human-wildlife conflict), identifies optimal intervention points, and simulates the long-term impact of conservation strategies for maximum biodiversity protection.

Adaptive capability

Mentor superpower

I possess a remarkable "superpower": Conservation Prioritization Optimizer. When students are designing conservation strategies, I can instantly activate a GAF-powered "Conservation Prioritization Optimizer." This tool analyzes various ecological datasets (e.g., species richness, threat levels, habitat connectivity) and visually ranks conservation areas or interventions based on their predicted impact on biodiversity protection and ecosystem services, allowing for optimal resource allocation in conservation efforts.

Your academic team

Guidance with depth and continuity

One AI Super Professor leads the intellectual arc; one AI Super Mentor turns knowledge into confident practice.

AI Super Professor

Prof. Dr. Mariana Jardim

Meet your professor Open the classroom

AI Super Mentor

Dr. Christina Roberts

Conservation Biology, Data Science, GIS and Remote Sensing, Genetics, Population Modeling, Leadership in Conservation Organizations.

Meet your mentor Open the classroom

Same faculty and level

Related programs

Named lists

Named lists for this house

Core. Bachelor, Master and Doctorate by duration. Enrolment is not open. Nothing here is a sale.

Duration	Bachelor	Master This programme	Doctorate
9 months · Fast track	15000 EUR	12000 EUR	15000 EUR
12 months · Recommended	18000 EUR	15000 EUR	18000 EUR
15 months · Standard	21000 EUR	18000 EUR	21000 EUR
18 months · Flexible	24000 EUR	21000 EUR	24000 EUR
21 months · Extended	27000 EUR	24000 EUR	27000 EUR
24 months · Part-time	30000 EUR	27000 EUR	30000 EUR

These are the owner lists. Enrolment is not open. Nothing here is a sale.

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Digital Ecology and AI for Biodiversity Conservation

Academic focus

Practical focus

Internships in technology companies or environmental organizations

Roles as conservation data scientists or GIS specialists

Consultancy in advanced digital ecology and AI for biodiversity conservation

Support roles in academic research projects on digital ecology

Chief Conservation Scientist for environmental organizations or research institutions

Data Scientist specializing in conservation genetics

AI Scientist in Digital Ecology

Researcher in Digital Ecology and AI for Biodiversity Conservation

Cultivating an interdisciplinary approach, integrating environmental science, computer science, and biology

Developing strategic thinking for digital ecology and AI for biodiversity conservation

Enhancing problem-solving through the analysis of complex conservation challenges

Critical thinking for a comprehensive and nuanced understanding of Digital Ecology and AI for Biodiversity Conservation

Research leadership

Applied mentorship

Professor research lens

Mentor practice lens

Professor superpower

Mentor superpower

Prof. Dr. Mariana Jardim

Dr. Christina Roberts

Find the program that expands your universe.