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topics: Free space optical communication Visible light communication DSP for coherent optical communication Machine learning and AI-native physical layer design Optical reconfigurable intelligent surfaces
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. MATLAB, C/C++, Python. Highly motivated and keen on working in an international and interdisciplinary team. Applicants with strong background in the following fields are preferred: Machine Learning Formal
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. Implement and optimize data representations and pipelines suitable for machine learning and uncertainty quantification. Collaborate with AI/ML experts to design and test inference methods that map
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 hours ago
machine learning methods like machine learning interatomic potentials is a preferred qualification. This preferred start date is January 2026 and can be negotiated. The initial appointment is for one year
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Posting Open Date Posting Close Date Qualifications Minimum Education and Experience The candidate should hold a PhD degree in Computer Science, Information Systems, Computer Engineering, or a related field
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have: Experience with AI and machine learning for medical images Experience working with data from multiple hospitals An interest in rare disease research. Additional information Informal enquiries
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dynamical systems), epidemiological modelling, data analysis (statistics, machine learning). • in scientific programming (preferably Python, Matlab, R) Genuine interest in the analysis and modeling
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well as opportunities for conference attendance and collaborations with other research organisations. Key Skills, Experience and Qualifications PhD in machine learning, computer vision or a related field. Established
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Type Staff Job Description Major/Essential Duties of Job: 1. Develop machine learning or physical based models for plant water stress quantification. 2. Develop machine learning models for crop mapping
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will work as a member of an interdisciplinary team (including experts in machine-learning and microbiology) to establish microfluidics-enabled microscopy assays on single bacterial cells to determine