Finally, there is also the European Lab for Learning and Intelligent Systems (ELLIS) programme.

ELLIS PhD Programme
ellis.eu/news/ellis-p...
Deadline 31st Oct 2025

In addition there is

CDT in Dependable and Deployable AI for Robotics
www.cdt-d2air.uk
Deadline 14th Nov 2025

CDT in Designing Responsible Natural Language Processing
www.responsiblenlp.org
Deadline 7th Jan 2026

We are fortunate to have several Centres for Doctoral Training (CDTs) here which cover full funding and scholarships for 4 years, e.g.,

CDT in Machine Learning Systems
informatics.ed.ac.uk/cdt-in-machi...
Deadline 11th Dec 2025

We have some fantastic invited speakers lined up:
sites.google.com/g.harvard.ed...

In addition, we have a paper track for both novel unpublished and published work (deadline Oct 10th):
sites.google.com/g.harvard.ed...

Very cool!

Check out the paper for more details:

Feedforward Few-shot Species Range Estimation
arxiv.org/abs/2502.14977
Lange et al. ICML 2025

This work was led by Christian Lange, with support from our amazing collaborators Max Hamilton, Elijah Cole, Alexander Shepard, Samuel Heinrich, Angela Zhu, Subhransu Maji, Grant Van Horn, and Oisin Mac Aodha.

FS-SINR is efficient. At test time, it can take an arbitrary number of observations (i.e., context locations) as input, along with optional metadata, and generate a predicted range in a single forward pass of the model.

We obtain better performance in the few-shot setting, i.e., where we have very limited observations for a species. In the x-axis in this plot we vary the number of observations provided to each model for a set of different species and on the y-axis we measure the quality of the range predictions.

We observe improved range prediction performance compared to existing methods, e.g., SINR from Cole et al. at ICML 2023 or LE-SINR from Hamilton et al. at NeurIPS 2024.

Top row: Gabar Goshawk
Bottom row: Black-naped Monarch

In this example, we see a prediction for FS-SINR using a single presence observation as input shown as a white dot (left). Conditioning the model with text (e.g. middle and right), can dramatically change the range predictions.

FS-SINR can be conditioned on in-situ presence observations for a species not seen during training in addition to text descriptions of their ranges or images of the species if available.

In the previous video, we illustrated test time range predictions for FS-SINR for the European Robin where we vary the number of presence observations (shown as white circles). As more observations are added, the predictions improve, becoming more similar to the expert range map (right bottom).

Paper:
arxiv.org/abs/2412.04120

Webpage:
iamsalvatore.io/cross_sdf/

Paper:
arxiv.org/abs/2411.17385

Project page:
danier97.github.io/depthcues/

Work led by Duolikun Danier:
danier97.github.io