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their effectiveness remains limited by the inherent constraints of fuzzing techniques. As an alternative, we propose exploring reinforcement learning (RL) as a promising approach for vulnerability assessment in SoCs
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–functional modeling of root system architecture. Phenomics data integration and high-dimensional trait analysis. Predictive breeding and quantitative genetic modeling. Machine learning approaches to genotype
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are still learning the important role they play in the communication between the plants and their environment. These metabolites may impose direct effects, e.g. as repellents, but they can also participate in
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: Sustainable Futures – Environmental Intelligence Post Duration: 12 Months, whole-time Post For an information package including full details of the post, selection criteria and application process see https
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Months, whole-time Post For an information package including full details of the post, selection criteria and application process see https://ore.ucc.ie/ The University, at its discretion, may undertake
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modeling, and AI experts, as part of the international project M2 LInES (https://m2lines.github.io) . You can read about some of our recent work here: Dheeshjith et al. 2025 , Duncan et al. 2025 , Pedersen
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continuous improvement in a constructive, learning-oriented environment. Application Instructions: Interested individuals should submit the following: A cover letter demonstrating interest and availability; A
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Publish high-impact research in leading journals and present findings at international conferences on energy systems and machine learning Collaborate with industry partner to tackle challenges of practical
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learning, and data warehousing. Commitment to quality, integrity, confidentiality and compliance. Excellent leadership, organizational, project management and problem-solving skills. Strong interpersonal
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Data Scientist (Artificial Intelligence). We now invite applications for the captioned post. Duties and Responsibilities Develop and apply advanced artificial intelligence and machine learning models