Sort by
Refine Your Search
-
candidate in the exciting area of multiscale and multiphysics modelling of sustainable fibrous composites, with additional focus on uncertainty quantification and machine learning. Information The context
-
that values innovation, rigor, and translational impact. Where to apply Website https://www.academictransfer.com/en/jobs/355928/phd-opening-reinforcement-learn… Requirements Specific Requirements
-
, particularly integer programming, e.g., vehicle routing and packing problems and heuristics; simulation; data-driven modelling; decision support systems; AI (reinforcement learning, machine learning). Motivation
-
techniques); applying them to solve real-world problems (e.g., gradient estimation challenges in experimental design and reinforcement learning). You’ll have plenty of space to shape the project to your
-
; Develop system architecture and training strategy to enable the FM to learn from heterogeneous MRI data in terms of data source purpose and physical location in the scanner; Develop efficient techniques
-
urban resilience. This is your chance to make a real impact! Information The energy and the climate crisis reinforced the urgency of energy efficiency in residential buildings in Europe. To meet its
-
to learn new techniques. Internship Experience: An internship or collaboration outside academia is considered a pre. Inclusive Outlook: We welcome applications from researchers who will add to the diversity
-
techniques that identify when a model is operating in such high-risk regions and uses this information to design new, privacy-preserving learning and explanation methods. Instead of producing generic
-
high infrastructure costs. We will explore (1) learning-based techniques (e.g., LLMs, agents) to capture the intent behind code changes, (2) defining new metrics for test "quality" that go beyond code
-
the world. Engineering Productivity Metrics This track will investigate how we can customize typical software engineering metrics to usefully reflect progress for machine learning engineers. Not all