Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
-
Field
-
: This PhD project will develop model- and data-driven hybrid machine learning material models that capture the complex, nonlinear, path- and history-dependent behaviour of materials. The goal is to create
-
chemical reaction networks with robotic systems and analytical science. You will also learn how to programme robotic systems and how to implement aspects of deep learning and neural networks for reservoir
-
actively with the collaborative research environment at the Centre for Learning and Teaching. Organisation The University of Groningen is a research university with a global outlook and a long academic
-
youth in schools, including social media data donation. As a PhD candidate, your tasks will include: instrument selection; setting up the social media data donation in collaboration with experts
-
team consisting of scientific staff and PhD candidates from different disciplines and will collaborate with a small team of software developers working on AI prototypes and the required supportive
-
depth. You organise your work efficiently, take initiative, and are able to work independently when needed. You’re open to feedback and eager to learn from it, and you enjoy collaborating within
-
scientific programming and numerical / statistical analysis of simulated and observed data. Candidates should be able to demonstrate motivation and a strong eagerness to learn, and have the ability to both
-
over 500 academic and support staff, we teach and conduct research in the fields of art, history, language, culture and communication, using innovative methodologies and collaborating closely across
-
independent research, participate in collaborative projects, and contribute to the geography of outer spaces. The research is funded by an incentive grant and will be conducted under the supervision of Dr Alana
-
., machine learning, stochastic dynamic programming, simulation). Affinity with (food) supply chain management is preferred. To collaborate with and to co-supervise MSc thesis students and internship students