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University of California, San Francisco | San Francisco, California | United States | about 2 months ago
, and biostatisticians. We seek a data scientist to join our team to develop AI/ML-based algorithms to support clinical decision making, hospital performance improvement efforts, and more. This position
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subjects, including Programming, Algorithms, Computer Logic and Architecture, Web and Mobile Development, Human-Computer Interaction (HCI), Green and Sustainable Software Engineering, Software Engineering
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intelligent sensing, followed by detection of the important events.In the light of autonomous decision making, the project aims at developing machine learning algorithms for knowledge extraction from data
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processing capability; 3. Implementation of pre-processing, anomaly detection, and self-calibration algorithms; 4. Integration with IoT communication systems selected in Task 3.2; 5. Laboratory and relevant
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find some of our publications here: https://i.giwebb.com/research/computational-biology/ Required knowledge A solid grounding in artificial intelligence and machine learning. Learn more about minimum
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learning algorithms for closed-loop optogenetic control of neural circuits (DC2). The appointed DCs will participate in an international research team as part of the EU-funded Marie Skłodowska-Curie Actions
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postdocs, tenure-track positions, tenured positions, and positions for distinguished professorship. Candidates in areas including, but not limited to, Quantum Algorithms, Quantum Machine Learning, Quantum
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integration, and system integration for next-generation wireless systems. We envision programmable and intelligent cellular networks that can be dynamically and algorithmically instrumented and optimized
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framing for NLP tasks, rigorous model evaluation, the design of end-to-end algorithmic workflows, assessment of methodological trade-offs and challenges, and the implementation of NLP systems using high
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evolution across different genomic regions by developing interpretable and efficient methods in comparative pangenomics, leveraging machine learning methods and statistical analysis (https://cgrlab.github.io