Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
with, learning methods for robotics would be a plus • Experience with robotic systems and/or aerial robots is a plus Where to apply Website https://emploi.cnrs.fr/Offres/Doctorant/UMR5216-VIRFAU-064
-
computational models and machine learning methods, as well as experience in repertoire data analysis. Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UMR8023-CLAMAR-001/Default.aspx Work
-
the molecular identity and function of specialised skin cells that become remodelled above the olfactory placode, and to determine how they acquire the properties required for orifice formation. The project will
-
mobile base, an arm, a gripper, a learned policy, a safety module). Each agent runs its own specialized solver and is coordinated to a common, dynamically feasible plan through distributed optimization and
-
decision systems for pricing and market strategies. In particular, algorithmic repricing tools allow real-time price adjustments based on predefined rules or machine-learning techniques. While recent
-
, statistics and scientific computing. * Proficiency in a scientific programming language, particularly Python, and the ability to develop reproducible processing procedures. * Knowledge of machine learning and
-
, GC-MS/MS, and advanced NMR approaches. - **Activity 4:** Multivariate statistics and machine learning to identify microbial and chemical biomarkers of resilience and reveal the interactions linking
-
to correlate polymerisation kinetics, macromolecular architecture, morphological evolution and drug encapsulation mechanisms. Beyond experimental work, the project will integrate machine learning approaches
-
on developing deep learning methods for the reconstruction and physical analysis of ATLAS experiment data. The selected candidate will develop innovative analysis methods for the reconstruction and physical
-
remaining biologically interpretable? The PhD candidate will design and apply integrative computational workflows using methods such as multi-omics integration, spatial modelling, representation learning