Sort by
Refine Your Search
-
Employer
- University of Luxembourg
- 3IA Côte d'Azur
- CEA
- Luxembourg Institute of Health (LIH)
- European Magnetism Association EMA
- French National Institute for Health Research (INSERM)
- IMT Mines Ales
- Inria, the French national research institute for the digital sciences
- Institut Pasteur
- Universite de Montpellier
-
Field
-
French National Institute for Health Research (INSERM) | Lille, Nord Pas de Calais | France | 2 months ago
bioinformatics workflows in Python for the analysis of transcriptomic, proteomic, single-cell transcriptomic, and interactome datasets. Design and implement Big Data and machine learning approaches using Python, R
-
Cardioembolic Stroke Risk Stratification using AI Accelerated Patient-Specific Blood Flow Simulation
wall motion will be extracted from dynamic 4D flow MRI (20 phases per cycle) via an in-house deep learning-based segmentation tool. Mean flow velocities in the pulmonary veins have also been measured by
-
in the field of operational research and/or machine learning algorithms would be a plus. In accordance with the commitments made by the CEA to promote the integration of disabled people, this job is
-
) to join our team and contribute to ongoing research projects. Key Accountabilities Acquire technical and theoretical knowledge through close supervision and training; Perform cell culture and image-based
-
of language learning and experience of doing qualitative research Ability to work in a multidisciplinary research environment Good analytical and problem-solving skills Experience of and interest in
-
their dynamics to be formulated as systems of linear ordinary differential equations. Bayesian Optimization and Reinforcement Learning methods will be employed to solve the inverse problem of shape and flexibility
-
pipelines, as well as developing new algorithms and data-processing routines. This relates also to new methods such as wearables, markerless motion capture, machine and deep learning (ML/DL) and artificial
-
(NIMS, Seagate). The student will be trained on a femtosecond laser bench, and learn extensively about aspects such as spintronics, ultrafast optics, near field microscopy, material growth and clean room
-
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