Sort by
Refine Your Search
-
Listed
-
Category
-
Country
- United States
- United Kingdom
- Germany
- Sweden
- Netherlands
- Norway
- France
- Denmark
- Spain
- Portugal
- Belgium
- Australia
- United Arab Emirates
- Switzerland
- Poland
- Singapore
- Austria
- China
- Canada
- Hong Kong
- Luxembourg
- Finland
- Czech
- Vietnam
- Ireland
- Morocco
- Italy
- Romania
- Estonia
- India
- Andorra
- Croatia
- Cyprus
- Latvia
- Lithuania
- New Zealand
- Brazil
- Greece
- Slovenia
- South Africa
- Ukraine
- Chile
- Japan
- Saudi Arabia
- Armenia
- Bulgaria
- Hungary
- Indonesia
- Israel
- Kenya
- Malta
- Qatar
- Taiwan
- Worldwide
- 44 more »
- « less
-
Program
-
Field
- Computer Science
- Medical Sciences
- Engineering
- Biology
- Economics
- Science
- Mathematics
- Chemistry
- Social Sciences
- Arts and Literature
- Business
- Education
- Psychology
- Humanities
- Materials Science
- Earth Sciences
- Electrical Engineering
- Environment
- Linguistics
- Law
- Physics
- Design
- Philosophy
- Sports and Recreation
- Statistics
- 15 more »
- « less
-
-theory-based and recently proposed Moiré Plane Wave Expansion approaches. A significant part of the project is focusing on the development of novel machine learning protocols and workflows based on a large
-
), the sorption of PFAS and heavy metals onto natural nanoparticles will be investigated in situ using a dedicated field exposure method developed by our team, complemented by laboratory experiments and machine
-
will develop novel machine learning and artificial intelligence (ML/AI) methods for genomics data, especially: large-scale single-cell genomics data, high-definition spatial genomics, digital pathology
-
the development and application of probabilistic inference methods and machine learning techniques for quantitative uncertainty modeling and for the integration of heterogeneous climate data
-
computational mechanics and scientific machine learning. The successful candidate will work on the design of hybrid, physics-informed modeling and identification frameworks for complex dissipative material
-
experience in scientific programming is a plus. • Experience in constructing Machine Learning potentials would be appreciated. Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UMR5254
-
at: https://www.umu.se/en/department-of-computing-science/ Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data driven models
-
heavily relies on empirical determination of key model parameters. By combining protein structure descriptors, molecular simulations, and machine learning, this PhD project seeks to predict ion-exchange
-
neurons. Responsibilities and tasks This PhD project aims to develop, verify, and benchmark learning rules in networks of complex spiking neuron models in the application field of geolocalization: Building
-
research and innovation agenda by: Conduct applied or fundamental research and publish the results in high-quality conferences and journals; Developing Computational Intelligence (e.g., Machine Learning and