Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
- Computer Science
- Engineering
- Medical Sciences
- Biology
- Economics
- Science
- Materials Science
- Mathematics
- Earth Sciences
- Business
- Chemistry
- Linguistics
- Environment
- Arts and Literature
- Electrical Engineering
- Physics
- Social Sciences
- Psychology
- Education
- Humanities
- Law
- Philosophy
- Sports and Recreation
- 13 more »
- « less
-
Engineering of Catalysts for Hydrofunctionalization Reactions: From Selectivity Control to a Predictive Model” project financed from the funds of Priority 2 of the European Funds for a Modern Economy Program
-
without shortages. The project will explore the use of historical demand and supply data, along with auxiliary information, within a Predict-then-Optimize (PtO) framework. This PtO framework will leverage
-
for defense, aerospace, and critical infrastructure. Energy generation and storage systems modeling, optimization, and control, with emphasis on reliability, affordability, and national security. Experimental
-
workflows that integrate modern AI and machine learning concepts (e.g., surrogate models, adaptive sampling strategies) into the drug discovery pipeline to increase throughput and predictive accuracy
-
applications (https://www.cbr.washington.edu/analysis) to perform statistical analyses relevant to fish, dam, water, and natural resource management. The Software Engineer will play a lead role in the
-
broader portfolio of academic affairs and data science initiatives, including AI integration, predictive modeling, statistical analysis, machine learning, and analytics infrastructure development. A major
-
, triage, knowledge base documentation, and escalation practices-ensuring work is prioritized, transparent, and delivery is efficient and predictable. Position Description Provide day-to-day Tier 1 support
-
Infrastructure? No Offer Description The PhD candidate will work on the development of advanced statistical and machine learning methods for time series prediction, with applications mainly in the field of traffic
-
production lines can reconfigure in real-time, in collaboration with domain experts (e.g. operators, planners, designers) that are supported by digital twins, AI, and predictive analytics. Process equipment
-
spans quantum mechanics, statistical physics, and deep learning and aims to enable AI-guided predictions of synthesizable and functional materials such as energy storages, catalysts, smart-alloys, energy