Sort by
Refine Your Search
-
Listed
-
Category
-
Country
- United States
- United Kingdom
- Portugal
- Netherlands
- France
- Germany
- Sweden
- Spain
- Belgium
- Norway
- Denmark
- Singapore
- Italy
- Morocco
- Australia
- Finland
- Switzerland
- Czech
- United Arab Emirates
- Poland
- China
- Ireland
- Canada
- Austria
- Luxembourg
- Romania
- Japan
- Brazil
- Estonia
- Hong Kong
- Andorra
- Croatia
- Greece
- Lithuania
- Malta
- Saudi Arabia
- Slovenia
- 27 more »
- « less
-
Program
-
Field
- Computer Science
- Engineering
- Medical Sciences
- Biology
- Economics
- Science
- Materials Science
- Mathematics
- Earth Sciences
- Environment
- Chemistry
- Business
- Psychology
- Humanities
- Education
- Electrical Engineering
- Law
- Linguistics
- Physics
- Arts and Literature
- Social Sciences
- Philosophy
- Statistics
- Sports and Recreation
- 14 more »
- « less
-
, or predictive modelling. • Experience working with secure research environments (e.g., TREs, data enclaves). Applicants should send the following documents during the application: a. Cover letter highlighting
-
materials to enhance the cell robustness. Work plan The work plan for the PhD thesis will be divided in three main steps: 1) A chemo-mechanical model will be built to predict the crack initiation and
-
health. Please see our website for more information: gvnlab.bme.columbia.edu We expect the Staff Associate III to lead the development and application of advanced computational models to simulate, predict
-
modelling photonic devices and physical reservoir computing systems. The activities within the project will benefit from synergies with other projects in the group as well as with other activities
-
Kogias as part of his ERC Starting project titled CloudNG (https://cordis.europa.eu/project/id/101220079 ). The post will be based in the Department of Computing at Imperial College London at the South
-
water quality parameters and predict cyanobacteria blooms in the Tietê system reservoirs. Activities: 1. Develop machine learning models for estimating water quality parameters via remote sensing; 2
-
the flexibility and power of NNs with the ability of LMMs to robustly learn from structured and noisy (non i.i.d.) data, applying them on the prediction of both plants and human phenotypes. These models will
-
types will change under different climate change scenarios based climate projections. This framework will be ultimately included in a flood prediction model, which will be developed within the VIDI
-
related topic and need to have expertise in working with data from the National Drug Treatment Monitoring System (NDTMS), working with government and expertise in quantitative prediction modelling
-
) for predictive modeling. Collaborate with neuroscientists, biostatisticians, and clinical researchers within BBRC and external partners. Contribute to manuscript preparation, presentations, and dissemination