Sort by
Refine Your Search
-
Listed
-
Category
-
Country
- United States
- United Kingdom
- Germany
- Sweden
- France
- Norway
- Portugal
- Singapore
- Spain
- Netherlands
- Belgium
- Denmark
- United Arab Emirates
- China
- Italy
- Australia
- Canada
- Luxembourg
- Switzerland
- Hong Kong
- Austria
- Finland
- Czech
- Ireland
- Poland
- Japan
- Romania
- Estonia
- Cyprus
- Latvia
- Brazil
- India
- Morocco
- Saudi Arabia
- Lithuania
- South Africa
- Andorra
- Greece
- Taiwan
- Israel
- Slovenia
- Armenia
- Barbados
- Europe
- Iceland
- Malta
- Mexico
- New Zealand
- Slovakia
- Vietnam
- 40 more »
- « less
-
Program
-
Field
- Computer Science
- Medical Sciences
- Economics
- Engineering
- Biology
- Science
- Business
- Mathematics
- Education
- Psychology
- Materials Science
- Arts and Literature
- Social Sciences
- Chemistry
- Humanities
- Linguistics
- Earth Sciences
- Environment
- Sports and Recreation
- Law
- Electrical Engineering
- Physics
- Philosophy
- Design
- 14 more »
- « less
-
learning at the highest level and to convey the products of its efforts to the world. Connections working at Columbia University More Jobs from This Employer https://main.hercjobs.org/jobs/22156777
-
, electrical and computer engineering, data science, informatics, biomedical engineering, or a related field. Preferred: Demonstrated expertise in AI-driven drug discovery, machine and deep learning
-
architectures for TTS and ASR Entrenamiento de modelos a gran escala utilizando frameworks modernos de deep learning / Training large-scale models using modern deep learning frameworks Publicaciones en
-
, training, and evaluation of machine learning models applied to healthcare problems. Support research workflows within established Linux-based environments under technical supervision. Conduct exploratory
-
(e.g., software engineering, cybersecurity, program analysis, machine learning). Relevant professional experience in software security, program analysis, or AI-driven code analysis. Scientific track
-
for this research work is divided into the following phases: 1) Collaboration in annotating a set of data, with a view to creating learning sets for Machine Learning models. 2) Evaluation of performance with respect
-
using machine learning methodologies; (2) the extension of an existing CFD framework for multiphase modeling to the case of PEC systems; (3) the implementation within the framework of a description of
-
the development and application of probabilistic inference methods and machine learning techniques for quantitative uncertainty modeling and for the integration of heterogeneous climate data
-
learning workflows, and perform data quality control across multiple datasets. The ideal candidate will implement data science analytical models and machine learning models following established
-
A.I. and machine‑learning techniques where appropriate to improve forecasting, modeling, or analytical efficiency. Utilize Bloomberg or FactSet, including APIs, to support research and analysis. What