Sort by
Refine Your Search
-
Listed
-
Employer
- Nature Careers
- CNRS
- CEA
- European Magnetism Association EMA
- American University of Paris;
- BRGM
- European Synchrotron Radiation Facility
- IMT MINES ALES
- Institut National Polytechnique de Toulouse
- The American University of Paris
- UNIVERSITY OF VIENNA
- Université d'Orléans
- Université de Technologie de Belfort-Montbéliard
- 3 more »
- « less
-
Field
-
candidates may be asked to teach: Introductory programming classes Core undergraduate CS classes such as: Human Computer Interaction, Database Applications, Algorithms and Data Structures, Software engineering
-
topological textures. A wide range of applied functionalities could be considered: terahertz sources, unconventional computing and AI, security components, telecommunications, sensors, memories etc.
-
techniques and the structure of bilevel problems in large-scale settings. Objectives The goal of this postdoctoral project is to develop scalable blackbox optimization algorithms tailored to bilevel problems
-
to accelerate evaluation of costly simulations Genetic algorithms and other evolutionary techniques to generate a diverse set of high-performing solutions. You will design and implement new optimization
-
openness to interdisciplinary collaborations Expertise in some area of computer science such as computational complexity, algorithms, data structures, logic in computer science and AI, semantics, theory
-
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
-
, including image acquisition, processing, analysis, and interpretation Develop and validate new imaging techniques, algorithms, or software to improve diagnostic accuracy and patient outcomes Collaborate with
-
robots, avatars, social bots, virtual assistants, or AI-driven systems such as chatbots, recommender algorithms, or generative AI. The position focuses on how communicative processes are shaped by and
-
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
-
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 performances and their limitations