Early career researcher: Are you thinking about writing an ERC , Emmy Noether Proposal, or Lower Saxony AI Group application, and are you interested to work with us at @uni-goettingen.de and @goettingen-campus.de
If you recently finished your PhD in ML for life science and are looking for a new job early next year, please apply! Göttingen is a lovely town and a great scientific environment.
Probably not. Last month the convention center hosted the annual Society for Neuroscience meeting, which regularly attracts more than 30,000 attendees
sfn2025.org
🛫 On my way to San Diego for #NeurIPS. Touch base if you want to meet up and chat about neuroAI, postdoc opportunities in Germany or just hang out!
📣 Paper alert: We present dense attentive probing (DeAP), a method to measure the representation quality of various vision backbones for dense prediction tasks. It uses small, parameter-efficient readouts with learnable masks to generate dense predictions from backbone features of any size.
I‘m at NeurIPS Mexico and happy to chat about comp neuro, video understanding, multimodal and foundational models
If you are in San Diego - check out our posters together with Nina, Finn, Chase and more members of @aecker.bsky.social and @sinzlab.bsky.social
More about the papers in the thread ⬇️
🔬 Exciting PostDoc Opportunity! 🐁🧠
We - the @sinzlab.bsky.social (sinzlab.org) and @trose-neuro.bsky.social (troselab.de) Labs - are seeking an experimental postdoc to work at @unibonn.bsky.social with cutting-edge miniature 2-photon microscopy and gaze tracking in freely behaving mice.
Deadline for applying with us at #ELLISPhD program is in 10 days (Oct 31). We're looking for highly motivated people working at the interface of machine learning and neuroscience.
If you're interested in doing your PhD with us, please apply via the ELLIS PhD program!
We're looking for talented people interested in the intersection of machine learning and neuroscience.
Touch base with me if you have any questions. I’m happy to chat!
🎯 Göttingen is a small university town (around 120k people) and very family friendly. Everything within the city can be reached by bike within 15 min.
🌍 Göttingen is located in the center of Germany and Europe, in a region known for its green hills, lovely small towns with half-timbered houses and the Harz Mountains. Well-connected through motorways and high-speed trains.
🎓The @goettingen-campus.de is highly collaborative, has a great variety of data-intensive research in natural and life sciences, social science and humanities, including top-notch places like @mpi-nat.bsky.social, @mpsgoettingen.bsky.social, MPI-DS, @primatenzentrum.bsky.social, @dlr-en.bsky.social
The position is embedded in the recently founded Lower Saxony Center for AI and Causal Methods in Medicine (CAIMed) caimed.de/en/ as well as the Campus Institute Data Science (CIDAS), Göttingen.
💡It's a deliberately broad call. Areas of interest include: RL; VR, AR & digital avatars; Embodied AI, NLP; HCI; Causal models; Time series models; Multimodal data integration; Image analysis and CV; Computational and simulation science; Visualization; HPC and Energy-efficient AI.
📢 We have an opening for a tenure track assistant professor position (W1 t.t. W2) in AI in our CS dept @uni-goettingen.de, Germany.
⏰ Application deadline: Sept 19
🗣️ Please share!
uni-goettingen.de/en/697924.html
📢 Fully-funded PhD opportunity!
Explore the links between social & communication networks in ring-tailed lemurs!
Exciting fieldwork, interdisciplinary team, and innovative technologies! Apply now and join @primatenzentrum.bsky.social & @unigoettingen.bsky.social for this cutting-edge research!
In Lurz et al., ICLR 2021 we did quite some analysis on scaling and generalization across animals in the context of visual response prediction (incl. behavioral modulation) with @sinzlab.bsky.social and @andreastolias.bsky.social: openreview.net/forum?id=Tp7...
A Perspective from the Ecker lab discusses the progress and challenges of using computer vision approaches for behavior studies of primates in natural environments.
www.nature.com/articles/s41...
...and of course – shame on me for the oversight – the great @philipp.hertie.ai
Thanks to co-authors Marissa Weis, Stelios Papadopoulos, @lhansel.bsky.social, Timo Lüddecke, Brendan Celii, Paul Fahey, Nuno da Costa, Forrest Collman, @csdashm.com , @clayreid.bsky.social, @sebastianseung.bsky.social, @viajake.bsky.social & Andreas Tolias. Funding: @erc.europa.eu & IARPA (12/12)
It's been great fun to be part of the MICrONS consortium. (11/12)
bsky.app/profile/alle...
Using the power of the MICrONS dataset, we could show that our functional digital twin of the neurons could predict the basal bias of cells in lower layer 4 without this model having access to any morphological information. (10/12)
www.nature.com/articles/s41...
We found a novel morphological trait in layer 4: neurons that are primarily located in V1 in a narrow stripe around the L4-L5 boundary. These neurons are atufted and avoid reaching into L5 with their basal dendrites. (9/12)
There morphological differences between primary visual cortex (V1) and higher visual areas (HVA): In layer 4, atufted neurons are primarily located in V1, while tufted neurons are more abundant in areas AL and RL. (8/12)
Neurons in layer 2/3 show strong trends with increasing cortical depth: (1) decreasing width of their dendritic arbor and (2) smaller tufts. (7/12)
Dendritic morphologies vary with respect to three major axes: (1) the soma depth, (2) the total skeletal length of the apical dendrites and (3) the total skeletal length of the basal dendrites. (6/12)
Dendritic morphologies form mostly a continuum, with distinct clusters only in deeper layers (e.g. layer 5 ET neurons). A quantitative test using synthethic surrogate data suggested that from layer 2 throughout upper layer 5 no density gaps exist and dendritic morphologies change continuously (5/12)
Our learned embeddings capture the essence of the 3D morphology of neurons and reflect known excitatory cell types from mouse V1. (4/12)
We employed GraphDINO, a self-supervised method for learning representations of neuronal morphologies without relying on manual annotations. The model outputs a vector embedding for each neuron that captures the morphological features of its dendritic tree. (3/12)
