Sort by
Refine Your Search
-
Listed
-
Employer
-
Field
-
trading decisions under high price volatility. This PhD position focuses on designing, developing, and evaluating self-learning energy trading algorithms that are able to cope with these challenges. By
-
models and algorithms for learning from data. Meanwhile, the field of knowledge discovery and data mining has allowed us to obtain insights from large amounts of data for decades, and it is worth
-
interpretable models and algorithms for learning from data. Meanwhile, the field of knowledge discovery and data mining has allowed us to obtain insights from large amounts of data for decades, and it is worth
-
Vacancies PhD Position in Algorithmic Energy Trading Key takeaways In recent years, the energy sector has undergone changes that have a high impact on the dynamics in power markets. One
-
Vacancies PhD position on Stochastic geometric numerical methods Key takeaways Are you passionate about developing cutting-edge numerical algorithms at the intersection of geometry, stochastic
-
advantages for manipulation and locomotion, but current control algorithms do not fully exploit their capabilities. Most rely on approximations tailored for rigid systems or require extensive sensing and
-
operate safely around humans. They offer unique advantages for manipulation and locomotion, but current control algorithms do not fully exploit their capabilities. Most rely on approximations tailored
-
sufficiently “compact” (i.e. algorithmically small and computationally efficient) to enable incorporation in integrated PED models. The development of these compact models will involve collaboration with several
-
specialist collaborator to guarantee adequate integration of perception and action; advanced motion-planning and control algorithms, continuously refined via robotic digital twins, enable reliable handling
-
. The research unit Intelligent Systems (IS) in Computer Science is focused on the development of Data Science, Pattern Recognition and Machine Learning algorithms for interdisciplinary data analysis. For more