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of trees using AI methods on acoustic signals All details here: https://www.eu4greenfielddata.eu/content/download/201/2097?version=3 Context: Abiotic stresses (e.g. frost, drought, wind) cause significant
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operations. The PhD will develop and implement artificial intelligence and data-driven methods for early anomaly detection, root cause diagnosis, and failure prediction on industrial systems, leveraging
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competitive robotic behaviours Data-driven and learning-based control methods Embodied intelligence, where perception, control, and physical interaction are tightly coupled The successful candidate will work
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fundamental research towards the completion of your doctoral thesis: including preparing, conducting and recording the outcome of experiments; developing appropriate research methods; writing up results in
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[ed](: https://www.tcd.ie/hr/assets/pdf/academic-hours-public-service-agreement.pdf)ucation, research, and innovation, which has been inspiring generations of thinkers for over 400 years. Post Summary
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-year contract starting September/October 2026 https://www.eu4greenfielddata.eu/ APPLICATIONS OPEN ON Juanary 5th 2026 Are you an aspiring researcher ready to drive the digital and green transition in
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, including but not limited to fatty acid production, biostimulants, nutrient recovery, novel feedstock pretreatment methods, and integrated processes for enhanced product recovery. The PhD project is part of
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level courses (including courses on research methods). Moreover, our PhD students are supported to attend high-quality international workshops and summer schools. PhD students are expected to collaborate
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, Gaussian, etc.), force-field-based simulations software (LAMMPS, DL_MESO, etc), and Monte Carlo methods (self-programming or using software). Experience or strong interest in data-driven modelling and
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Engineering Telecommunications Engineering Data Science / Artificial Intelligence Computer Engineering Applied Computing Computer Science Or a closely related discipline Equipped with advanced scientific and