The inventors building
what comes next.

A community of researchers at the frontier. Your published work is your proof of capability. We help the right deep-tech teams find you.

US Patent App · 2024/0312847 A1
A system and method for real-time anomaly detection in distributed sensor arrays using sparse autoencoder architectures. Filed: Mar 2024. Inventors: Chen S., Osei A.
doi:10.1038/s41586-024-07218
Extended Data Fig. 4 — Binding affinity comparison across 847 protein complexes in the holdout validation set.
github.com/k-nakamura/qec-surface · README
# Surface Code Simulator Fast, hardware-accurate QEC simulation. ★ 2.4k · MIT License > pip install qec-surface
Algorithm 1 — Graph Encoder
Input: G = (V, E), dim d, depth L Output: h ∈ ℝ^(|V|×d) 1: for l = 1 to L do 2: aggregate neighbourhood
SBIR Phase II · UKRI · EP/X024891/1
Milestone 3 Complete — 94% capacity retention over 500 charge cycles in solid-state prototype. Phase II award confirmed: £750k.
Fig. 3 — Ablation Study
Removing the cross-attention layer yields a 7.2% performance drop, confirming its role in cross-modal feature alignment.
Acknowledgements
Supported by EPSRC EP/T026170/1 and a Royal Society University Research Fellowship (URF\R1\211218). Computing via ARCHER2.
Abstract
We present a scalable framework for learning transferable representations from heterogeneous scientific corpora across research domains.
The process

Three steps.

01
Connect your ORCID iD

One click. Your ORCID iD links directly to your verified publication record — no manual entry, no forms.

02
Asriel reads your work

Asriel reads your published work and builds a deep capability profile — what you've built, what domains you've pushed forward, what problems you can actually solve.

03
The right teams reach out

When a role matches your research depth, companies see you — ranked by fit, not by who submitted an application first.

The problem

Your work deserves better signal.

01
Your CV doesn't show what you can build

Years of building genuine expertise compressed into job titles and keywords. The depth of what you actually did, the problems you solved, the systems you built — none of it survives the format.

02
Your publications don't show who's looking

Google Scholar tells the world what you've published. It doesn't tell the deep tech team in Boston that they need exactly your work on sparse attention mechanisms, right now, this quarter.

03
Cold applications don't reward depth

The companies you'd thrive at are drowning in applications. Yours gets ranked by keyword match and submission speed, not by whether your research actually solves their problem.

The community

Who's already here.

Researchers whose published work, not their CV, got them matched to deep tech teams.

Computational Genomics
Dr. Sarah Chen
University of Cambridge
3rd year postdoc
Single-cell RNA-seqCRISPRProtein folding
9 publications
Quantum Error Correction
Dr. Kai Nakamura
Imperial College London
Final year PhD
Surface codesTopological qubitsFault tolerance
6 publications
Soft Robotics
Dr. Amara Osei
University of Edinburgh
2nd year postdoc
Bio-inspired designPneumatic actuatorsEmbodied AI
11 publications
Battery Materials
Dr. Elena Voronova
University of Oxford
Final year PhD
Solid-state electrolytesLi-ionSolid-state NMR
7 publications
Plasma Physics
Dr. Rohan Sharma
University of Manchester
1st year postdoc
StellaratorFusion energyMHD stability
8 publications
ML for Drug Discovery
Dr. Zara Al-Hassan
UCL
3rd year postdoc
Graph neural networksMolecular dockingAlphaFold
13 publications

Sample profiles — illustrative of the community.

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