Sort by
Refine Your Search
-
Listed
-
Category
-
Country
- United States
- United Kingdom
- Germany
- Sweden
- France
- Norway
- Portugal
- Singapore
- Belgium
- Spain
- Netherlands
- Denmark
- China
- Italy
- United Arab Emirates
- Canada
- Luxembourg
- Switzerland
- Australia
- Hong Kong
- Austria
- Finland
- Czech
- Ireland
- Poland
- Cyprus
- Japan
- Morocco
- Brazil
- Estonia
- Latvia
- India
- Saudi Arabia
- Lithuania
- South Africa
- Bulgaria
- Greece
- Romania
- Taiwan
- Andorra
- Israel
- Slovenia
- Armenia
- Barbados
- Europe
- Iceland
- Malta
- Mexico
- New Zealand
- Slovakia
- Vietnam
- 41 more »
- « less
-
Program
-
Field
- Computer Science
- Medical Sciences
- Economics
- Engineering
- Biology
- Science
- Business
- Mathematics
- Education
- Psychology
- Materials Science
- Arts and Literature
- Humanities
- Chemistry
- Social Sciences
- Linguistics
- Earth Sciences
- Environment
- Electrical Engineering
- Sports and Recreation
- Law
- Physics
- Design
- Philosophy
- 14 more »
- « less
-
, TensorFlow, HuggingFace). Model Development and Delivery Support Perform data cleaning, exploratory data analysis (EDA), and feature engineering. Train, evaluate, and compare machine learning models under
-
for part-time employment. Starting date: 27.03.2026 Job description:PhD position on physics-based machine learning modeling for materials and process design Reference code: 2026/WD 1 Commencement date
-
research, machine learning or artificial intelligence (e.g., large language models, EHR foundation models), causal inference (e.g., target trial emulation), and child health research. The research program
-
to reduce the cost of clean hydrogen to $1/kg by 2031. The project proposes to address key scientific challenges by using molecular simulations (reactive force fields like ReaxFF and machine learning
-
of interest to BNL and the Department of Energy (DOE). Topics of particular interest include: (i) development of novel machine learning models and adaptation of existing approaches for scientific applications
-
network structures. Methods from graph theory, machine learning, and artificial intelligence will be employed to model complex relational structures and identify patterns in high-dimensional data. The work
-
with leading machine learning frameworks and modern AI environments, including multi-GPU model training and large-scale inference on dozens to hundreds GPUs, are required. Additional Qualifications
-
based on machine learning tools for energy problems related to prediction. The application domains include both industry and climate changes. The first two months will be devoted to the study of
-
Computational Mechanics. Solid background in continuum mechanics and numerical modeling Strong interest in machine learning and scientific computing Experience with numerical methods for PDEs and data-driven
-
projects within the CUS related to urban sustainability, environmental monitoring, and urban resilience. Key Duties • Design and implement machine learning and deep learning models for hydrological