Melih Kandemir

The project is a PhD fellowship in the Centre for Machine Learning at the Data Science and Statistics Section of the Department of Mathematics and Computer Science (IMADA), University of Southern Denmark. The research focuses on probabilistic machine learning, with a particular emphasis on foundation-model-inspired approaches for multi-agent reinforcement learning. The PhD student will investigate new algorithmic principles that enable large populations of learning agents to solve open-vocabulary control tasks from sparse reward signals. The project aims to produce new theoretical insights into the algorithmic roots of autonomous behavior, alongside representative demonstrations in challenging simulation environments. The student will join the SDU Adaptive Intelligence Laboratory and work in a research environment described as vibrant and collaborative, with support from other lab members in both technical and social matters. IMADA is presented as a department that brings mathematicians and computer scientists together to support theoretically grounded data science research, and the successful candidate will contribute to high-quality research papers with the rest of the lab. The position is embedded in a strong academic setting on the main campus in Odense, where the department is part of a broader university community with active data science research and coursework.

Opportunity related to soft robotics , robot control , and machine learning at the ADIN Lab in collaboration with Silvia Tolu's lab . The post highlights research on how soft robots can be robustly controlled in low-energy regimes and tested on real hardware . The announcer is Melih Kandemir , Professor of Machine Learning at the University of Southern Denmark . This appears to be an academic recruitment-style opportunity, but the post does not specify whether it is a PhD, postdoc, or master's opening, nor does it provide detailed eligibility criteria, funding, or a deadline in the text shown. Interested candidates should follow the linked SDU career site posting for the full details and application instructions. Relevant background likely includes robotics, control, machine learning, and experimental work with hardware.

No summary provided for this position.