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Field
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learning-based computer vision algorithms and software for object detection, classification, and segmentation. Key Responsibilities Participate in and manage the research project together with the PI, Co-PI
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, and laboratory/field experiments Cost-benefit analysis, General equilibrium model, Advanced econometrics Machine learning techniques including text analysis, and related subjects Well-versed in
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analysis, machine learning, and interactive and collaborative systems. The prospective PhD candidate will work in close cooperation with staff and our current PhD students. PhD research fellows receive
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(e.g., Docker, Kubernetes, cloud/edge environments). Demonstrated expertise in AI, distributed computing, machine learning, or systems software design. Strong background in software engineering
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, output validation and reporting. Developing integrative strategies for a diverse set of data, integrating the outcomes to inform future projected trend analysis. Applying statistical and machine learning
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. To learn more about ongoing research in Saha laboratory please visit https://www.kumc.edu/school-of-medicine/academics/departments/radiation-oncology/research/saha-lab-of-radiation-biology.html. Job
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discipline or a related field - Strong demonstrated verbal and written communication skills Desired Qualifications* - Familiarity with manufacturing systems and processes - Experience with machine learning and
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Equinor. The position is in the Digital Signal Processing and Image Analysis (DSB) research group, Section for Machine Learning, Department of Informatics. For more information about the position, see https
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acquired from the field survey Develop machine learning models for prediction and recommendation Job Requirements: Preferably PhD in Computer Science or related field. Expertise in computer programming
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, develop research proposals and attract additional external fundings when appropriate. Job Requirements: Preferably PhD in Computer Engineering, Computer Science, Electronics Engineering or equivalent