Sort by
Refine Your Search
-
of Learning and Development (LEAD- UMR-5022), at the Université Bourgogne Europe, CNRS (https://lead.ube.fr/ ) . To apply, please submit: - CV - Cover letter describing your interest in the position
-
, machine learning and deep learning. The project Motivation: Interpreting the genome means modeling the relationship between genotype and phenotype, which is the fundamental goal of biology. Achieving
-
the creation of high-precision digital twins. Activity 1: Integration of Photometric Stereo in Meshroom - Implement processing nodes for normal field and intrinsic color estimation. - Integrate deep learning
-
researchers with ample experience in MEG/EEG data analysis, BCIs, signal processing, deep learning for brain imaging analysis, biomedical statistics, dynamical systems and research on motor control. The lab has
-
on studying the principles of neural computation through recurrent neural networks, dynamical systems theory, and machine learning. - Develop mathematical and computational models of neural networks - Analyze
-
of sea turtles - Developing innovative machine learning methods to analyze the sounds associated with these behaviors - Automating the processing of audio and visual data to optimize the quantity and
-
using deep learning or causal learning methods. Candidates must have solid experience with large spatial and temporal datasets, large model manipulation, and HPC. The candidate must also have experience
-
Vision Profiler (UVP), and to analyse its spatial and temporal variability. This will be done by combining different data sources and machine learning (ML). Data used for this ML approach include - a
-
, involving expertise in optics, electronics, image and data processing, chemistry, and biology. With the support of several European funding programs, the team is building a data science and machine learning
-
team at the Laboratoire d'Informatique de Grenoble (LIG). GetAlp conducts research in NLP, machine learning, evaluation, and interpretability. The project will be supervised by Maxime Peyrard (CNRS