The newly funded Trento Joint Laboratory for Quantum Science and Technologies (Quantum at Trento, Q@TN) invites expressions of interest from highly motivated senior scholars for the position of Director.
MITRE is seeking highly qualified and motivated scientist/engineer to join MITRE’s Quantum “Moonshot” Program. The candidate should have intimate knowledge or experience in quantum optics and integrated photonics. In this role, you will apply your technical experience in quantum physics and photonics to develop entangled photon sources, chip-based quantum photonic circuits, or quantum optical measurement systems.
We invites applications for different positions in experimental or theoretical studies of dark matter detection with optical atomic clocks. The candidate can choose between:
the Theme 1: Dark matter direct-detection experiments with the sensor network made of optical atomic clocks,
the Theme 2: Development of a new generation of optical sensors with enhanced detection limit for the variations in fine structure constant and other fundamental constant
The newly created Centre for Quantum Optical Technologies at the University of Warsaw, Poland, seeks to appoint group leaders who will develop ambitious and innovative research effort exploring quantum phenomena, such as superpositions and entanglement, in optical and optically controlled system, with the long-term aim of their practical utilisation. The appointments will include start-up packages with fully funded junior research positions, access to state-of-the-art research infrastructure, and a possibility to apply for equipment upgrades.
The selected candidate will be responsible for designing, building, training, and deploying machine learning models on Xanadu’s cutting-edge specialized quantum computing hardware. As part of our Quantum Machine Learning team, they will participate in multiple aspects of machine learning research and development targeted to near-term quantum devices. Other duties may include the training of traditional (non-quantum) machine learning models for comparison and benchmarking purposes.