Sort by
Refine Your Search
-
profile PhD in an environment-related field followed by experiences as PostDoc in related interdisciplinary research contexts, optimally with research that aimed to work on environmental evidence synthesis
-
train robust machine learning (ML) algorithms without exchanging the actual data. The benefits of such a decentralized technology over personal and confidential data are multiple and already include some
-
revisit discretization methodologies in view of modern requirements and computational capabilities. The candidate will focus on developing mesh generation algorithms meeting the following criteria
-
training of an artificial intelligence algorithm capable of automatically segmenting the bony structures of both healthy and fractured tibial plateaus. This will serve three main purposes: 1) Enable
-
algorithms will be developed to extract discriminative and predictive features from a multimodal dataset consisting of digital histopathological images, lung CT images, clinical, genomics, and multiproteomics
-
. Contact Information: Mariángeles KOVACS-AREVALO (postdoc): mariangeles.kovacs-arevalo@pasteur.fr
-
: 305,000 euros total funding over the project duration, including: • 199,000 euros for contractual collaborators (PhD students, postdocs, research assistants) • 106,000 euros for operational expenses
-
, transport, or defense. On the technical side, we aim at combining statistical latent variable models with deep learning algorithms to justify existing results and allow a better understanding of their
-
laboratory and collaborative meetings. Collaborate actively with scientists performing wet bench experiments (PhD, postdocs). Interact productively with other bioinformatic engineers or researchers on Institut
-
analyzed. The tensor model structure estimated by suitable optimization algorithms, such as that recently developed in [GOU20], will be considered as a starting point. • Exploiting data multimodality and