Sort by
Refine Your Search
-
Employer
-
Field
-
to match the scale of neural circuits identified within the Drosophila larvae, facilitating comparative analysis between artificial and biological networks. Required skills: This project will involve a
-
The detection of out-of-distribution (OoD) samples is crucial for deploying deep learning (DL) models in real-world scenarios. OoD samples pose a challenge to DL models as they are not represented
-
Inria, the French national research institute for the digital sciences | Saint Martin, Midi Pyrenees | France | about 1 month ago
. The project that we propose is part of this growing AI4Science movement, focusing on a key challenge in experiments: figuring out which model parameters best match the data we observe (Figure 1). More
-
foundation models. You will bring your expertise in biology and data science to provide crucial scientific direction, ensuring we tackle the most important problems in drug development and translational
-
datasets generated by our collaborators; ensure timely delivery to meet network/dynamic modeling needs. 1. Tissue & cellular phenotyping of intestinal ageing across life stages in vertebrate models
-
generated by our collaborators; ensure timely delivery to meet network/dynamic modeling needs. 1. Tissue phenotyping: histology on paraffin sections of intestine and distant tissues at different stages
-
all stages (data ingestion, preprocessing, model training, deployment), Explore ML-based automation for security checks and adversarial testing, Validate proposed solutions through industrial case
-
Category Mathematics, information, scientific, software Contract Internship Job title Large language models for automatic bug finding in source code analysis H/F Subject JOIN US, TO DO WHAT
-
Operations (MLOps) has become essential to managing the lifecycle of machine learning (ML) models, enabling continuous delivery, automation, and reproducibility. However, the rapid adoption of MLOps has
-
Operations (MLOps) has become essential to managing the lifecycle of machine learning (ML) models, enabling continuous delivery, automation, and reproducibility. However, the rapid adoption of MLOps has