Sort by
Refine Your Search
-
Listed
-
Category
-
Country
- United States
- United Kingdom
- France
- Portugal
- Germany
- Sweden
- Netherlands
- Norway
- Spain
- Belgium
- Denmark
- Italy
- Singapore
- Australia
- Finland
- Ireland
- Luxembourg
- Switzerland
- Morocco
- Canada
- China
- Poland
- Czech
- 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
-
software, including design tools, CAD drafting, information systems, construction management software, computerized maintenance management systems (CMMS), hydraulic modeling, Bluebeam, and the full Microsoft
-
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
-
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
-
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
-
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
-
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
-
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
-
and validate them across multiple cancer cohorts Link CIN programs to outcomes and therapy response using large public datasets and modern predictive modeling Integrate CIN signatures with functional
-
AI to predict safety outcomes for multiple targets and combination therapies Collaborate with research teams and data scientists to design data-driven strategies using machine learning/AI methods