Sort by
Refine Your Search
-
play a pivotal role in Dynamic Nuclear Polarization (DNP) sensing, a technique that significantly enhances nuclear spin signal. Candidates will contribute to the development of new classes of organic
-
that interface with human stem cells, investigate cellular and molecular mechanisms governing development and pathology, lead experiments from conception through publication in high-impact journals, mentor
-
systems (ITS). In particular, the successful candidate will conduct cutting-edge research in: Developing physics-informed neural networks (PINNs) for complex dynamical systems modeling and observer design
-
. The central goal of the laboratory is to study the neural mechanisms involving dynamic RNA modifications during cognitive development and decline. To achieve this, research projects rely on the use of a
-
the safety and robustness of human-robot interaction. The postdoc will work on projects focusing on one or more of the following: Learning-Based Control: Developing reinforcement learning, imitation learning
-
through computer simulations and/or experimental validation. The PDA is expected to actively disseminate results through publications in high-impact journals and presentations at leading international
-
simulation software, e.g., Wireless Insite, Zemax Proven track record of publications in relevant IEEE journals and conferences The candidates will be considered until the position is filled. To be considered
-
. This involves the development of mathematical models for signal transmission/reception, derivation of performance limits, algorithmic-level system design and performance evaluation via computer simulations and/or
-
management Cognitive radio or adaptive communication systems, including dynamic spectrum access, band selection Heterogeneous network architectures, including terrestrial and non-terrestrial networks Deep
-
for physical systems. The postdoc will work on projects focusing on one or more of the following: Robot Learning & Autonomy – Developing algorithms that allow robots to learn (via exploration or imitation) from