At a Glance
- Tasks: Conduct innovative research using machine learning on Earth Observation data to understand forests.
- Company: Join a leading academic institution with a focus on environmental science and technology.
- Benefits: Fully funded PhD scholarship covering tuition fees and a stipend for living expenses.
- Other info: Collaborate with top researchers and gain valuable experience in a dynamic research environment.
- Why this job: Make a real impact on forest conservation and climate change through cutting-edge research.
- Qualifications: Strong background in machine learning and a passion for environmental science.
The continuous release of Earth Observation (satellite) data and the emergence of Machine Learning methods open up new possibilities for understanding forests. These large datasets provide complementary information on 3D structure (GEDI lidar, BIOMASS P-band radar, NISAR L-band radar) and high spatiotemporal resolution (Sentinel‑1 C-band radar, Sentinel‑2 multispectral). State-of-the-art foundation models (e.g., AlphaEarth, TerraMind) are currently being evaluated for different applications, but the inclusion of temporal components and newly available datasets in foundation models remains limited. There is also a need for accounting noise in Deep Learning models and quantifying uncertainty in real-world applications.
This PhD studentship (scholarship) leverages large-scale Earth Observation data to evaluate and advance machine learning algorithms for one of the following application areas:
- “Characterising forests variations in relation to distance from pre-Columbian earthworks in the Amazon forest, Brazil”, co-supervisor Prof Ted Feldpausch, University of Exeter, UK
- “Predicting mixed-forest composition and/or understanding intra-variability of same forest types at European level”, co-supervisor Dr Emily Lines, University of Cambridge, UK
- “Quantifying forest planation damage and supporting planning after a cyclone or tropical storm in New Zealand”, in collaboration with Interpine Group Ltd, NZ
The prospect candidate is requested to choose one application (listed or relevant) and write a 300 word proposal on how innovative algorithms can tackle it. Applicants are encouraged to reach out to the lead supervisor, Dr Milto Miltiadou (m.miltiadou@exeter.ac.uk), to gain insight into the specialised data available and the associated challenges of each proposed project. The studentship will be awarded based on merit. Both Home and International Students are eligible. The PhD funding includes tuition fee coverage and stipend.
PhD Scholarship: ML for Earth Observation Forests employer: Technical University of Denmark
The University is an exceptional employer, offering a supportive and collaborative work environment that prioritises employee well-being and professional growth. With the flexibility of hybrid working and generous leave entitlements, including 36 days off, staff are encouraged to maintain a healthy work-life balance while contributing to the academic success of students in the Faculty of History.
Contact Details:
Technical University of Denmark Recruitment Team
StudySmarter Expert Advice🤫
We think this is how you could land PhD Scholarship: ML for Earth Observation Forests
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✨Utilise University Career Services
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✨Show Off Your Stuff on GitHub
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We think you need these skills to ace PhD Scholarship: ML for Earth Observation Forests
Some tips for your application 🫡
Show Off Your Technical Skills:For a data science internship, we want to see those analytical skills shine! List your programming languages, like Python or R, and make sure to highlight any relevant projects or courses you've completed. If you've dabbled with tools like Pandas, NumPy, or machine learning algorithms, don’t hold back – include those in your CV!
Share Your Curiosity in Your Cover Letter:As an intern, your motivation and eagerness to learn are key! In your cover letter, talk about specific data science concepts that excite you and how this internship at Technical University of Denmark will help you grow. Share what you hope to achieve and how you plan to tackle real-world data problems - we love enthusiasm!
Include Any Relevant Certifications:If you've earned any certifications, such as from Coursera or DataCamp, make sure to include these in your application. They show us that you're proactive and committed to expanding your data science skillset. This could make a real difference in how we assess your application!
Keep It Relevant and Concise:Remember, as an intern, you don’t need to have decades of experience. Focus on showcasing relevant coursework, personal projects, or even related volunteer work in data science. Keep your CV and cover letter concise but impactful – we appreciate clear and straightforward communication!
How to prepare for a job interview at Technical University of Denmark
✨Brush Up on Your Coding Skills
As a data science intern, you might get grilled on your programming skills. Expect to tackle some coding challenges using languages like Python or R. We recommend practising basic algorithms or data manipulation tasks so you can show off your tech skills with confidence.
✨Show Off Your Projects
Prepare to discuss any projects you’ve done, whether in your studies or on your own time. Having a strong portfolio of data analyses or machine learning models will really set you apart. We can use platforms like GitHub to showcase your work to impress Technical University of Denmark.
✨Know Your Stats and ML Basics
Brush up on your statistics and machine learning concepts because interviewers love to dig into this! Be ready to explain your understanding of algorithms or how you would approach a given data problem. This will highlight your theoretical background alongside your practical skills.
✨Be Eager to Learn and Adapt
Internships are all about potential and growth. Make sure you convey your eagerness to learn and adapt to new tools or methodologies. Show Technical University of Denmark that you’re not just looking for experience, but that you're keen to contribute and grow within the team.
