19 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"CEA-Saclay" positions at SciLifeLab
Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
). The project focuses on developing computational models for cancer risk assessment, integrating multiple types of data and risk factors. The main objective is to design and apply machine learning and deep
-
information about us, please visit: www.dbb.su.se . Project description The candidate will develop machine learning (ML) strategies, primarily revolving around interpretable ML and generative AI, to study
-
. Our research is focused on cell biology, spatial proteiomics and machine learning for bioimage analysis. The aim is to understand how human proteins are distributed in time and space, how this affects
-
in artificial intelligence (AI) to join our growing biomedical innovation team. In this pivotal role, you will lead and contribute to the design, development, and deployment of machine learning
-
data types (transcriptomics, proteomics, imaging). AI/ML Applications: Applying machine learning or AI to predict gene function or discover functional relationships from perturbation data. FAIR
-
dependent predictive deep learning models, and physical mechanistic models (thermodynamic and kinetic models etc.). Examples of suitable backgrounds: machine learning, programming, mathematics, physics. You
-
data types (transcriptomics, proteomics, imaging). Knowledge on AlphaFold for models in structural protein analysis/proteomics AI/ML Applications: Applying machine learning or AI to predict gene function
-
registries and biobanks. The applicant is expected to have a strong computational focus on innovative development and application of novel data-driven methods relying on machine learning, artificial
-
(transcriptomics, proteomics, imaging). Knowledge on AlphaFold for models in structural protein analysis/proteomics AI/ML Applications: Applying machine learning or AI to predict gene function or discover functional
-
innovative development and application of novel data-driven methods relying on machine learning, artificial intelligence, or other computational techniques. More specifically, at NRM this research will be