1,362 machine-learning "https:" "https:" "https:" "https:" "RAEGE Az" positions at Nature Careers
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tumor cells and host or immune cells in the tumor microenvironment and periphery to identify predictive biomarkers and develop novel immunotherapeutic approaches for solid tumors (e.g., TCR T cells, CAR T
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tabletop, functional, and full-scale drills to test institutional preparedness. Participate in a critique of each drill record lessons learned, and develop improvement plans to address identified shortfalls
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peripherals). Experience supporting business teams around IT needs. Prefer experience with Windows, Apple, and Linux. Licensure, Registration and/or Certification Required by SJCRH Only: Certification in A
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peripherals). Experience supporting business teams around IT needs. Prefer experience with Windows, Apple, and Linux. Licensure, Registration and/or Certification Required by SJCRH Only: Certification in A
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Professor (W3 / W2 with Tenure Track to W3) for Materials and/or devices for Photonics and Quantum T
quantum technologies. This research can be complemented by digital methods of process simulation and optimization, as well as machine learning. Requirements include an outstanding PhD in materials science
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measure gravitational effects on entangled photons for shining light onto the interface of quantum physics and gravity? Can we exploit quantum photonics technology for novel quantum machine learning
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basic science to its effective translation for preventing or alleviating disease. Candidates for this joint appointment should have research interests focused in computational immunology/AI/Machine
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workshop for digital and conventional manufacturing (CNC, laser machining, additive manufacturing). At Principal level, you will also contribute to management and long-term technical strategy, infrastructure
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machine learning techniques, will quantify groundwater recharge and groundwater resilience. Your responsibilities: Analyse the dynamics of hydrological connectivity of soil moisture using gridded soil
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analysing multimodal deep learning models for time-specific cancer risk and time-to-event prediction by integrating imaging with longitudinal Electronic Health Record (EHR) signals. Building scalable