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skills Desirable criteria Experience of atomistic modelling of ferroelectric materials Experience in development and application of machine learned potentials * Please note that this is a PhD level role
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Candidates should have a PhD in physics or a related field, and will be evaluated on their academic credentials and their prior research record. Preferred Qualifications Experience with machine learning (ML
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of atomistic modelling of ferroelectric materials 2. Experience in development and application of machine learned potentials * Please note that this is a PhD level role but candidates who have submitted
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terms of research and education, covering all aspects of computer science, including artificial intelligence, machine learning, data sciences, algorithms, databases, cloud computing, software engineering
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emerging spectrum (FR3, MMW, sub-THz/THz) Extreme massive MIMO communications Analog, digital and hybrid beamforming architectures Reconfigurable intelligent surfaces Machine learning for wireless
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) Machine learning models for physical behavior prediction and manipulation strategy adaptation Real-world robotic systems, including dual-arm manipulators and soft robotic grippers These positions are part
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) Corrosion behavior (electrochemistry & high-temperature oxidation) In-situ monitoring of AM processes Computational skills in: Phase-field modeling, Machine Learning, FEM, DEM, COMSOL Alloy design (CALPHAD
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, including artificial intelligence, machine learning, data sciences, algorithms, databases, cloud computing, software engineering, networking, operating systems and security. Job Description We are seeking
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algorithms for speech enhancement using state-of-the-art machine learning techniques. You will design and evaluate models that leverage phoneme-level or discrete speech representations and conduct experiments
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and radar remote sensing, climate time series, and hydrological models. The work will employ machine learning and explainable AI techniques to improve flood prediction under different hydroclimatic