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Field
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Officer will be mainly responsible to process geophysical data and analyze the data using machine learning algorithms. The position involves in conducting research, supervising undergraduate students, and
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at the intersection of statistics, machine learning, data analytics and modern AI algorithms. This includes, in particular, statistics for high-dimensional and complex data, stochastic optimization
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RF front-ends (FR2, FR3, V-band) into end-to-end ISAC demonstrators. Implement and optimize advanced signal processing algorithms for joint communication and sensing. Analyze experimental datasets
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learning algorithms and specific applications in GeoAI In-depth knowledge of spatial/GIS programming, preferably in Python A proven track record of application of machine learning to real-world problems
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of early pre-malignant development of ovarian carcinoma and development of early detection/screening tests/algorithms of ovarian cancer. Qualified candidates must have a Ph.D. degree in Biology, Cellular
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algorithms and parallel/distributed variational algorithms in AI/ML for application workflows and large-scale HPC and QC systems Develop quantum machine learning (QML) algorithms for optimization of multi
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to synthesize sequencing-based analysis results e.g.,Rshiny, d3, plotly, ggplot2. A solid understanding of statistics and experience in the implementation of machine learning and statistical inference algorithms
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with the latest sensors (camera and LiDAR sensors), is available for the work. What you will do Development of algorithms for 3D multi-object tracking based on heterogeneous sensor data fusion (standard
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. Specifically, its activities will be developed in the following areas: - Design and creation of technical content and audiovisual material about IA algorithm, machine learning techniques and IoT. - Support to
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further extensions subject to good performance and availability of research funding. Key Responsibilities: Performing literature review and background study on AI Develop algorithms and system designs