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of system and data confidentiality and complete any other requirements. Desirable skills: Experience in programming (C, python, or similar). Knowledge in machine learning or distributed systems. How to apply
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accelerate the global shift to electric mobility. This PhD will build an AI-driven fleet-scheduling framework that learns from battery data in real time and optimises charging, operations and maintenance
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components. The resulting data will be used to train a machine learning (ML) model, enabling automated and efficient beamline alignment. This technology has the potential to significantly enhance
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experience with programming (e.g., Python), machine learning, or educational data is beneficial, it is not a strict requirement. The project provides ample opportunities to develop these skills over time. What
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. Understanding of or curiosity about machine learning, AI, or cloud computing tools used in agricultural analytics. Interest or experience in working with industry, government, or multidisciplinary research teams
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an equivalent discipline) expertise in statistical and machine learning approaches, with the ability to apply advanced methods to complex environmental and agricultural datasets proficiency in R and/or
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microfluidic fabrication and experiments 3D printing machine learning. Demonstrated programming skills (Matlab, C++, or Python). Desired Demonstrated ability to work independently and to formulate and tackle
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to simulate key optical features and explore optimal alignment of beamline components. The resulting data will be used to train a machine learning (ML) model, enabling automated and efficient beamline alignment
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will also bring: A PhD or EdD (or equivalent) in applied mathematics, data science, machine learning or a related discipline, with demonstrated excellence in tertiary teaching across these areas
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for research leadership. Strong experience in at least two of the following areas: deep learning and/or computer vision continual learning vision language models Experience in developing comprehensive benchmarks