-
develop machine learning approaches (deep learning) to understand the eco-evolutionary mechanisms underlying biological diversity from environmental (phylo)genomic data. - Methodological developments in
-
), whose objective is to extend the HLA-Epicheck model, originally developed within the framework of a PhD thesis, and to implement new deep learning approaches to assess donor–recipient compatibility in
-
» AlgorithmsYears of Research ExperienceNone Additional Information Eligibility criteria - PhD in one of the following areas (or related fields): * Machine learning / deep learning * Quantum computing / quantum
-
, statistics, machine learning and deep learning. The project Motivation: Interpreting the genome means modeling the relationship between genotype and phenotype, which is the fundamental goal of biology
-
of autonomous mobile machines integrating perception, reasoning, learning, action and reaction capabilities. The team's main research areas are: architectures for autonomous robots, human-robot interaction
-
that interaction represents the foundation of active learning and fosters acquisition and retention of knowledge, as opposed to passive reception in traditional teaching. Some benefits of MR are now well established
-
perception for robotics; machine learning. o An interest for approaches based on foundation models. o Proficiency in open-source libraries: Pytorch or equivalent, OpenCV, Open3D, PCL, etc. o Programming
-
. • Strong knowledge of signal processing methods and machine learning. • Familiarity with regulatory and ethical constraints in research involving sensitive data. • Ability to work closely with
-
machine learning tools. The postdoctoral fellow will contribute to various aspects of the project, such as: * developing new theoretical and numerical approaches for determining the thermodynamic and
-
. - Knowledge in programming, data treatment, electron diffraction simulations, mathematical skills, knowledge about machine learning and artificial intelligence is a plus. Website for additional job details