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data sources such as UK Biobank and eventually come up with algorithm useable for the early detection of Alzheimer’s disease (AD) and Parkinson’s disease (PD). Nature of Work: In this project, we will
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, or computer algorithms for law enforcement and sentencing. The position has a teaching load of 2:2 -- i.e., four semester-long courses over the academic year -- and will have a joint teaching commitment to GOV
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working with ML/DL platforms and algorithms. Track record of working with domain experts, researchers, and stakeholders to support diverse science and engineering applications. City: Piscataway State: NJ
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. The successful candidate will play a pivotal role in a project centered around variational quantum algorithm in the near-term, especially on innovating advanced error mitigation or detection techniques to solve
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of concepts from quantum information to quantum materials; algorithmic aspects of quantum computing; topological aspects of magnetism; connections between condensed matter and high-energy physics
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Europe in electrochemistry, energy storage, power electronics, process engineering, smart sensors, IoT, and solar power. Together, we pursue the common goal of accelerating the decarbonization of energy
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are now looking for a Doctoral candidate (m/f/d) on the development of optical clocks Your tasks Enrollment at a partner university (Humboldt-University Berlin, Group for Integrated Quantum Sensors) as a
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manufacturing - smart sensors, digital twins, and AI-driven food quality control and processing; Food safety - supply chain tracking/monitoring, blockchain Biotechnology: Host strain screening and engineering for
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-guided medical applications, with a focus on advanced robotics. You will work directly with clinical data to design robust, efficient deep learning algorithms that maximize the information extracted from
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algorithms. We welcome applications from individuals with experience in: Experience developing deep learning models for real-time image/video segmentation, object tracking, reinforcement learning. Deep