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Field
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asap, focused on ‘Quantum Machine Learning’, with the objective of investigating hybrid classical-quantum and quantum inspired algorithms. The tasks will include the design and implementation
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transcriptomic technologies and biological data interpretation is a plus. Familiarity with optimizing cell segmentation algorithms for enhanced accuracy and efficiency, as well as experience with tools/packages
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patterns may mimic those observed in irrigated croplands. Key Responsibilities: Conduct research to develop algorithms and methodologies for mapping irrigation patterns using satellite imagery. Investigate
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to develop algorithms and methodologies for mapping irrigation patterns using satellite imagery. Investigate methods for detecting the timing and frequency of irrigation events from time-series remote sensing
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analysis to translate THz signals into optical material properties such as refractive index and absorption coefficient. Development of machine learning algorithms for material classification. Exploration
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. Data Acquisition & Analysis: Captures high-resolution live calcium imaging data using advanced microscopy systems. Applies computational tools and algorithms to analyze calcium signaling patterns and
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vulnerability assessment for satellite communications links Design, implement, and evaluate AI/ML‑based algorithms for real‑time detection, classification, and localization of jamming and spoofing signals using
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3GPP compliant 5G/6G NR NTN OFDM waveforms Develop and analyse signal processing and/or machine learning algorithms for joint channel, delay, Doppler and carrier phase estimation, remote object ranging
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interface, and all the way to quantum algorithms and applications. The long-term mission of the programme is to develop fault-tolerant quantum computing hardware and quantum algorithms that solve life
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georeferenced data on pest outbreaks. The Scholar will use these data in collaboration with computer scientists to develop machine learning algorithms for the detection and management of biotic stress. The crops