Sort by
Refine Your Search
-
Category
-
Employer
-
Field
-
The Machine Learning for Integrative Genomics team at Institut Pasteur, headed by Laura Cantini, works at the interface of machine learning and biology, developing innovative machine learning
-
Artificial Intelligence (applied mathematics, computer science, etc.), or a thesis defense scheduled for 2025. • Research contributions in deep learning, statistical learning, natural language processing (NLP
-
, including 180 permanent staff (researchers, professors, engineers, technicians, and administrative personnel) and around 180 non-permanent staff (PhD students, postdocs, and fixed-term contracts). Each year
-
for such applications. To respond to these challenges, this project aims to investigate automated decision making based on machine learning. The candidate (H/F) will propose and validate centralized as
-
, engineering, bioinformatics, machine learning, artificial intelligence) to support minimally invasive and targeted preventive and predictive medicine capable of limiting age-related functional disorders
-
by exploiting foundational machine-learning potentials such as MACE, SevenNet, or Orb-V3. The predictions will then be progressively refined and verified by DFT and, ultimately, tested experimentally
-
Saclay) and address the impact of those contractile programs in dystrophic mouse models. Histological analysis and single molecule FISH will be used for a deep characterisation of the different mouse
-
interdisciplinary, and together we contribute to science and society. Your role Multi-omics data integration and workflow improvement Development and application of machine learning-based algorithms
-
-on-chip platforms. 🤝 Why join us ? …. By joining the IRIG Institute, you will become part of a dynamic and innovative research environment where you will have the opportunity to learn, grow and play a key
-
used to develop networks capable of self-learning and self-optimisation, adapting to real-time changes in traffic and demand. The successful candidate will contribute to designing solutions that optimise