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systems, multi-function radar, AI/ML algorithms rely on high performance digital signal processing and real-time computing to provide high-fidelity results in an actionable timeframe. The ARRC intends
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developing a machine learning (ML) algorithm for the automated analysis of the above-mentioned mass spectra. Desirable: - knowledge in the field of Planetary Sciences - very good written and spoken English (C1
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techniques to answer fundamental questions about our Universe. Candidates are expected to demonstrate an exceptional commitment to the teaching and mentoring of students. Research Group Overview: Prof. Aishik
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or more of: the use of micro/nanofabrication and materials characterization tools; computational multi-physics/electromagnetics modelling and/or the application of machine learning algorithms; experimental
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mathematics. The applicant should be skilled at implementing new models and algorithms in a suitable software environment, with documented experience. Experience in applying or developing machine learning
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corresponding knowledge in another way. A successful candidate should have excellent study results and a strong background in mathematics. The applicant should be skilled at implementing new models and algorithms
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Networks, and ICT Services & Applications. Your role The APSIA Group (Applied Security and Information Assurance), led by Prof. Peter Y A Ryan, has recently been granted the project "Quantum LDPC codes with
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generation of enhancement algorithms tailored for endoscopic videos, with a focus on applications in real-time interventions. Development efforts will prioritize both enhancement quality and computational
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for the position of assistant professor in the project NCN MAESTRO "Challenging problems in partial differential equations inspired by cutting-edge algorithms in statistics and machine learning
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the closing date for applications. The applicant must have good programming skills, excellent knowledge of algorithms, numerical methods, and signal processing Mandatory experience and formal training: signal