Sort by
Refine Your Search
-
Listed
-
Category
-
Country
- United States
- United Kingdom
- Germany
- Sweden
- France
- United Arab Emirates
- Australia
- Netherlands
- Finland
- Mexico
- Canada
- South Africa
- Singapore
- Spain
- Belgium
- India
- Portugal
- Switzerland
- Austria
- Denmark
- Italy
- Norway
- Japan
- Saudi Arabia
- Hong Kong
- Brazil
- Ireland
- Luxembourg
- Estonia
- Morocco
- Turkey
- Algeria
- Papua New Guinea
- Czech Republic
- Iceland
- Malaysia
- Qatar
- South Korea
- 28 more »
- « less
-
Program
-
Field
- Computer Science
- Biology
- Statistics
- Engineering
- Medical Sciences
- Earth Sciences
- Environment
- Education
- Chemistry
- Physics
- Psychology
- Mathematics
- Electrical Engineering
- Materials Science
- Social Sciences
- Business
- Economics
- Science
- Design
- Arts and Literature
- Humanities
- Sports and Recreation
- Law
- Linguistics
- 14 more »
- « less
-
-03033600, co-financed by the European Union through the COMPETE 2030 Programme, of Portugal 2030, under the following conditions: Scientific Area : Machine Learning Admission requirements
-
performance utilizing a variety of tools including life-cycle assessment (LCA), machine learning techniques, and/or optimization methods. This position will carry out state of the art research into the
-
will be performed by the applicant by running multiple fermentation batches using a rational design of experiment (DoE). This data will then be used to train a model (either machine learning or
-
Science Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data-driven models for complex data, including temporal data
-
-preserving, machine-learning–accelerated scientific computing for plasma physics applications. In particular, the project involves developing data-driven collisional kinetic models and numerical schemes
-
Stanford University / SLAC National Accelerator Laboratory | Menlo Park, California | United States | 2 months ago
Position Description The SLAC National Accelerator Laboratory (SLAC) is seeking a Research Associate (RA) to work on Machine Learning (ML) and Artificial Intelligence (AI) for high energy physics
-
project will develop general self-supervised and unsupervised machine-learning methods for biological data where reliable labels are scarce, expensive, or impossible to obtain. The aim is to build methods
-
empirical, machine-learning, and physics-informed approaches for retrieving breaking-sensitive metrics from SAR and linking them to wave dissipation estimates derived from collocated sea-state information
-
quantum chemical methods and machine learning; developing quantum algorithms for computational chemistry on quantum computers; and applying existing and new computational methods to study multiscale
-
, machine learning and statistical methods as part of a close collaboration between Uppsala University, Westinghouse, and Vattenfall? Would you like to contribute to the development of fast, robust and