Sort by
Refine Your Search
-
Listed
-
Category
-
Country
- United States
- United Kingdom
- France
- Portugal
- Germany
- Sweden
- Netherlands
- Norway
- Spain
- Denmark
- Belgium
- Italy
- Singapore
- Australia
- Finland
- Ireland
- Luxembourg
- Switzerland
- Morocco
- Canada
- China
- Czech
- Poland
- Austria
- Japan
- Estonia
- Hong Kong
- United Arab Emirates
- Brazil
- Malta
- Vietnam
- Andorra
- Macau
- Saudi Arabia
- Slovakia
- Barbados
- Bulgaria
- Iceland
- Latvia
- Romania
- Slovenia
- 31 more »
- « less
-
Program
-
Field
- Computer Science
- Engineering
- Medical Sciences
- Biology
- Economics
- Science
- Materials Science
- Mathematics
- Earth Sciences
- Chemistry
- Environment
- Business
- Humanities
- Arts and Literature
- Law
- Linguistics
- Psychology
- Physics
- Social Sciences
- Electrical Engineering
- Sports and Recreation
- Education
- Design
- Philosophy
- 14 more »
- « less
-
simulations. Data-driven materials discovery: ML models for property prediction, materials design, or synthesis optimization. AI/ML methods development: Neural networks, graph neural networks (GNNs), generative
-
to the analysis of multi-omic data, models for predicting phenotypes using genotype data, biological data integration, etc. Participation in these projects will include scientific programming, data analysis
-
, please visit: https://qbm.genzentrum.lmu.de/application/ Tuition fees per semester in EUR None Combined Master's degree / PhD programme No Joint degree / double degree programme No Description/content
-
into European energy system models based on the institute's own open-source FINE framework https://github.com/FZJ-IEK3-VSA/FINE . Your tasks in detail: Implementing geothermal plants with material co-production
-
of the system, including laboratory testing and/or in situ monitoring campaigns. •Proposing predictive maintenance strategies based on the collected data and developed models, w ith the aim of optimising
-
of models like CNN, RNN, Transformers with some work in classical machine learning with XGBDTs is expected. Relevant work can lead to co-author publications and contributions to grant proposals. Tentative
-
the scientific supervision of Professor José Carlos Magalhães Duque da Fonseca. Grant duration: Initial duration of 3 months, with the predicted starting date in april 2026, on an exclusive basis eventually
-
efficiency and lifetime predictions under realistic operating conditions. Validating the developed models using experimental data from drivetrain test benches equipped with load, temperature, vibration, and
-
applicant will work with the ReXIl team, AIML, and 4DMedical to turn data into clinical impact. They will be responsible for developing algorithms for image analysis, creating predictive models for disease
-
applications of neural networks to the analysis of multi-omic data, models for predicting phenotypes using genotype data, biological data integration, etc.. Participation in these projects will include