University of Cambridge, University of Manchester | PhD in AI / Earth Observation / Environmental Science | ONSITE | UK home fee status only | £30k tax-free annual stipend
Our group at Cambridge works on Earth Observation foundation models (Tessera: v1 https://arxiv.org/abs/2506.20380, v2 https://arxiv.org/abs/2607.03949, https://geotessera.org/) and we've started a collaboration with Manchester to understand how they can be used to improve pollution models and then environmental decision making in general. This involves training new models and doing high-resolution inference across national or even global scales, as well as figuring out how to use that data to help policymakers make better decisions.
As part of this work we have a PhD studentship through the joint Manchester-Cambridge Centre for Doctoral Training for AI in Decision Making for Complex Systems. This involves one year at Manchester doing taught postgraduate study and then three years at Cambridge for the PhD.
Our work is all open source and involves solving significant compute and data challenges. Training our current-generation model takes ~250k GPU hours and a single year of global inference needs to move over 5.5 petabytes of data through the inference pipeline. This project would suit someone with strong machine learning and software engineering experience. We can help you build the atmospheric science and Earth Observation skills.
We are a highly collaborative group who spend our time not only on foundational research but also on developing applications with practitioners and decision makers. Our work sees use in many industrial, academic and government environments for problems like crop classification, tree species mapping and change detection.
His work has uncovered a number of really tricky bugs in the multicore runtime but what's brilliant is the reports normally come with a minimal reproduction. This makes working out the cause so much easier.
https://github.com/ucam-eo/geotessera has an image showing our embedding coverage at the moment. Blue areas we have complete coverage for 2024, green areas we cover 2017-2024. We're slowly trying to populate everything 2017-2024 but the constraint is GPU and storage at the moment - each year takes ~20k GPU/200k CPU hours and requires storing and serving 200 terabytes of data. The world is big!
If there is an area you would like prioritised, there's an issue template on the geotessera github repo which we can use to move regions around in the processing queue.
Thanks for your explanation. For my region, 2024 coverage is already available, which should be sufficient to get started. After looking into the library, I just want to make sure I understand the workflow correctly: I would use the Tessera interactive map to mark known locations of Giant Hogweed, label them, and export as GeoJSON; then train a k-NN model, make predictions, and finally export the results as a GeoJSON back to the map. Does that sound right?
...and if you do build this @ensocode, feel free to open a PR to https://github.com/ucam-eo/geotessera and I'll incorporate it as an example in the repo.
I was a lot more optimistic about Gabriel's model than he was. It is essentially a presence-only species distribution model where accuracy depends largely on assumptions around prevalence and which really needs some presence-absence data to calibrate.
As I mentioned in one of the other comments, the model is also only pixel-wise. That is, it is not using spatial information for predictions.
We did note several places during the trip that didn't contain bramble. The hotspot in the middle of the residential area was also entirely isolated.
For a proper evaluation you would need to be more methodological but as a sanity-check we were very happy with it.
One other thing to point out about the bramble model is that it is pixel-wise. That is each prediction is exclusively only what is within the 10 metre pixel (give or take the georeferencing error).
It might work. TESSERA's embeddings are at a 10 metre resolution, so it might depend on the size of the features you are looking for. If those features have distinct changes in colour or texture over time or they scatter radar in different ways compared with their surroundings then you should be able to discriminate them.
The easiest way to test is to try out the interactive notebook and drop some labels in known areas.
Is there a way to cluster the embeddings spatially or look for patterns isolated so some dimensions? (Again, way out of my wheel house)
What I mean is a vein is usually a few meters wide but can be hundreds of meters long so ten meter resolution is probably not very helpful unless the embeddings can encode some sort of pattern that stretches across many cells.
It's possible to use embeddings as input to a convolutional network and then train that using labels. We've done that for at least one of the downstream tasks in the TESSERA paper: https://arxiv.org/abs/2506.20380 to estimate canopy height.
The downside of that approach is that you need to spend valuable labels on learning the spatial feature extraction during training. To fix that we're working on building some pre-trained spatial feature extractors that you should only need to minimally fine-tune.
There is the issue of just how visible truffles are from space though, if they grow under cover. That said, it may still work because you can find habitats that are very likely to have truffles. We've had some promising results looking at fungal biomass.
That's actually a great idea! I wonder what kind of feature size would be needed though - TESSERA's embeddings are at a 10 metre resolution so for larger structures you might need some kind of spatial aggregation.
Hyperspectral data is really neat though it's worth pointing out that TESSERA is only trained on multispectral (optical + SAR) data.
You are very right on the temporal aspect though, that's what makes the representation so powerful. Crops grow and change colour or scatter patterns in distinct ways.
It's worth pointing out the model and training code is under an Apache2 license and the global embeddings are under a CC-BY-A. We have a python library that makes working with them pretty easy: https://github.com/ucam-eo/geotessera
Our group at Cambridge works on Earth Observation foundation models (Tessera: v1 https://arxiv.org/abs/2506.20380, v2 https://arxiv.org/abs/2607.03949, https://geotessera.org/) and we've started a collaboration with Manchester to understand how they can be used to improve pollution models and then environmental decision making in general. This involves training new models and doing high-resolution inference across national or even global scales, as well as figuring out how to use that data to help policymakers make better decisions.
As part of this work we have a PhD studentship through the joint Manchester-Cambridge Centre for Doctoral Training for AI in Decision Making for Complex Systems. This involves one year at Manchester doing taught postgraduate study and then three years at Cambridge for the PhD.
Our work is all open source and involves solving significant compute and data challenges. Training our current-generation model takes ~250k GPU hours and a single year of global inference needs to move over 5.5 petabytes of data through the inference pipeline. This project would suit someone with strong machine learning and software engineering experience. We can help you build the atmospheric science and Earth Observation skills.
We are a highly collaborative group who spend our time not only on foundational research but also on developing applications with practitioners and decision makers. Our work sees use in many industrial, academic and government environments for problems like crop classification, tree species mapping and change detection.
You can find out more here: https://www.findaphd.com/phds/project/from-satellite-to-stre... - if you have questions about the project you can contact me, Sadiq Jaffer, at [email protected] . For questions about the application process you'll want [email protected] .
Application deadline is Friday 4th of December with a course start date of September 2027.
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