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quantum memories, quantum light sources, quantum frequency conversion and quantum processing nodes. The current positions will be within a project aiming at developing quantum repeaters using solid-state
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commitment to integrating AI in their scholarship and instruction and may come from any discipline. A PhD is expected prior to the start date. The Postdoctoral Scholar will serve as one of the Initiative’s AI
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science/chemistry/biology or related disciplines, a PhD degree must be conferred in the year of employment in the project or within 12 years before 1 January of the year of employment in the project (career
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(editorial supervision and technical implementation). Your Profile: Doctorate/PhD in (Catholic) Theology or equivalent qualification, Very good written and spoken German (proof of proficiency must be provided
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Experience Doctoral (PhD) degree in Civil, Ocean, Mechanical Engineering or related field with a focus on Coastal Systems, Coastal Numerical Modeling, Nearshore Field Data Collection, Hydraulic Engineering
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distribution systems. Current research initiatives include developing advanced vibration analysis methodologies for wooden packaging systems, predictive modeling for packaging performance, and optimization
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of Electrical Engineering and Computer Science (EECS), in an international and dynamic environment with strong academic and industrial collaborations. You will have access to international supercomputing
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collaboration, this position is based on campus, full-time, at Harvard University. Remote work for this position is not possible. Basic Qualifications PhD in computer science, statistics, electrical engineering
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presentations, as well as actively initiating and participating in meetings with academic and industry partners. The postdoctoral candidate shall also be committed to contribute to the supervision of (PhD
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 2 months ago
the current limitations of existing T2V systems. A core focus will be on achieving fine-grained conditional generation via post-training with Reinforcement Learning (RL), allowing users to specify complex