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Field
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PhD candidate you will develop new ways to extract cosmic-ray physics from KM3NeT data. You will design and characterise reconstruction methods—both machine-learning-based and traditional
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independently. We value personal development: you will receive training in advanced computational techniques, machine learning, data analysis and scientific communication. You’ll have the opportunity to attend
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catalysts used in chemical processes. The successful candidate will combine state-of-the-art quantum chemical modelling alongside machine learning techniques and contribute to the development of predictive
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design and characterise reconstruction methods—both machine-learning-based and traditional—for the bundles of muons that reach the detectors, and apply them to data and simulations to constrain cosmic-ray
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. The project involves the development and modelling of hydraulic systems, with particular emphasis on energy-efficient system architectures and appropriate component selection. A key aspect is to incorporate
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, modelling, or computer-aided design (CAD) skills Specific Requirements Compliance with the MSCA Doctoral Network mobility rule Must not have a doctoral degree at the date of their recruitment Willingness
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methods in practice. This includes the ability to design, implement, and critically reflect on computational approaches such as machine learning models, large language models, and/or advanced data
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, and machine-learned force fields to describe ion transport and interfacial evolution. These models will be extended to mesoscopic and continuum scales (kinetic Monte Carlo, phase-field) to capture
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the AIM-PACE project, which aims at developing a new generation of safe-and-sustainable-by-design, bio-based and circular plastics by combining AI, microbial technology, computer modelling, and polymer
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” modeling solutions; we are open to and excited about applying all different types of statistical and ML techniques, from linear models to deep learning, depending on what best fits a given problem. The most