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Field
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candidate will perform prioritized Non-Targeted Assessment across diverse water matrices and case studies, while the AI4Science PhD will develop machine‑learning models that learn from and build upon
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to one or more of the following areas: Renewable energy systems and photovoltaic technologies Smart grids and power systems Battery energy storage systems Artificial intelligence and machine learning
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PhD in Computer Science, AI, Machine Learning or related field Experience Strong track record of publications in top-tier venues (e.g. CORE A*) Expertise in reinforcement learning, AI agents, and LLM
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research. Experience applying machine learning and statistical modeling techniques to large biological datasets for biomarker discovery, disease prediction, or host-pathogen investigations. Proficiency in
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/ computer vision and pattern recognition, including but not limited to biomedical applications Strong interest in applied machine learning, including but not limited to deep learning Experience utilising GPU
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environments Interest in industrial monitoring systems, smart sensors, and sustainable manufacturing Experience with sensor data processing or instrumentation systems Knowledge of machine learning or anomaly
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or other large-scale biological data), using statistical methods, pathway/network analysis or machine learning. The candidate will conduct integrative analyses of biomedical datasets, focusing on single-cell
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teaching duties. Requirements: Applicants should possess a PhD degree in Computer Science, Computer Engineering, Information Systems, or a related field, and sufficiently demonstrate abilities to conduct
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, including machine learning and language technologies, for the integration and analysis of clinical, advanced data harmonisation, and next generation research infrastructures. You will contribute to research
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such as Machine Learning, Natural Language Processing, AI in Education, Knowledge Representation, and Python-based analytical seminars at the BSc, MSc, and PhD levels. Responsibilities include assisting in