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Field
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, computational physics, computational materials science, inverse problems, signal processing, x-ray science etc. are encouraged to apply. Position Requirements PhD completed in the past 5 years or soon to be
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translating natural language specification into a symbolic representation (e.g. knowledge graph (KG) or logic program) and a symbolic solver computing the solution. Another example is the generation
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scienceEducation LevelPhD or equivalent Skills/Qualifications PhD in computer science Background in probability, Markov chains, MDPs Knowledge about reinforcement learning and planning are a plus but not necessary
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successful candidate will develop and apply computational approaches to chemical datasets, with artificial intelligence/machine learning (AI/ML) being a major focus. Many of the laboratory's interests center
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University of Southern California (USC) | Los Angeles, California | United States | about 1 month ago
outputs are biologically and clinically meaningful. Contribute to PI-led grant applications and mentor undergraduate/graduate students. Qualifications: Required: PhD in Computer Science, AI, Data Science
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essential. Most importantly, the candidate should be curiosity driven and willing to constantly learn new things! Qualification: PhD degree in Computer Science, Informatics, or Software Engineering obtained
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Two Postdoctoral Researchers in Cell Delivery-Based Beta Cell Replacement Therapy for Type 1 Diabete
30 years of experience in research and teaching in the fields of Computer Science, Cybersecurity, Artificial Intelligence, Robotics, Data Science, and Applied Mathematics. In this role, you will be
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framed within a recent Horizon Europe granted project 101093166 entitled A Multiplexed Plasmo-Photonic Biosensing Platform For Rapid And Intelligent Sepsis Diagnosis At The Point-Of-Care (AMBROSIA
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environmental mastitis in dairy herds by improving cubicle hygiene through intelligent, sensor-driven automation. By contributing to this research, you'll be at the forefront of precision livestock farming
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, including applied mathematics and computer science, experimental computing systems, scalable algorithms and systems, artificial intelligence and machine learning, data management, workflow systems, analysis