PhD candidate in AI in Ship Design, Construction and Operation

Descrizione

PhD candidate in AI in Ship Design, Construction and Operation Sintesi in italiano (traduzione automatica): L'Università NTNU, con sede a Trondheim, cerca un candidato per un dottorato di ricerca in intelligenza artificiale applicata alla progettazione, costruzione e operazione di navi. Il ruolo prevede di indagare come rappresentare la conoscenza relativa a diversi progetti navali, facilitando l'uso di metodi AI per migliorare l'efficienza nella progettazione e nell'operazione. Il candidato lavorerà a stretto contatto con l'industria marittima locale, affrontando problemi ingegneristici reali e utilizzando dati concreti. È richiesta una laurea in ingegneria navale o un campo correlato, e il candidato deve essere in grado di ottenere le autorizzazioni necessarie per collaborare con i partner industriali. La posizione offre l'opportunità di contribuire a un progetto innovativo che mira a unire dati di progettazione e operazione in un'unica base di conoscenza. This is NTNU NTNU is a broad-based university with a technical-scientific profile and a focus in professional education. The university is located in three cities with headquarters in Trondheim. At NTNU, 9,000 employees and 43,000 students work to create knowledge for a better world. You will find more information about working at NTNU and the application process here. The Department of Ocean Operations and Civil Engineering has a vacancy for a PhD candidate in AI in Ship Design, Construction and Operation. A PhD Position in Close Collaboration with the Maritime Industry A ship is designed once, but it is lived with for decades. Moreover, a ship design company rarely works with just one ship. It works with an idea of a ship, designs that get evolved, customised, and built each time differently. At any moment, different instances of that idea exist side by side: some still on the drawing board, some under construction, others already operating and sending back streams of sensor data from daily operation. The knowledge that connects these instances, and that connects design, construction, and operation more broadly, rarely lives in one coherent place. It is scattered across documents, tools, and people, out of reach of the AI methods that could otherwise make efficient use of it. This PhD position aims to investigate how such knowledge could be represented so it holds together across many instances and lifecycle phases of a ship, and becomes something AI methods, not only engineers, can work with directly. This includes examining how components, systems, and the dependencies between them might be captured in a shared structure, and comparing candidate approaches, such as ontologies, knowledge graphs, graph databases, or multi taxonomy databases, and the AI techniques needed to build and query them, to see where each fits, where each falls short, and whether a hybrid solution serves better. A good representation should not only describe a vessel as it is, but support AI-assisted inference: surfacing what else is affected by a change, what may be missing from an incomplete design, or what the likely consequences are elsewhere in the vessel. Whether such a representation should fit existing practices and tools, or justify changing them, is itself an open question, to be weighed against the time and value it can bring to the industry. Part of the motivation is that design and operational data, gathered across many vessels and many projects, could form one coherent body of knowledge that AI methods can search and learn from, helping surface relevant precedent and consequences for new designs rather than starting each one from scattered documents and individual experience. Turning that possibility into something concrete is part of the candidate's task, who will narrow this broad research space toward specific applications and evaluate them against real design tasks. The work is carried out in close, ongoing cooperation with the local maritime industry, grounded in real vessels, real data, and real engineering problems. Candidates should be available and able to obtain any necessary clearance to work directly and regularly with the project's industrial partners. In short, this PhD position aims to investigate: How one design, one idea of a ship, exists as many instances at once: on paper, under construction, or already at sea Why the knowledge connecting design, construction, and operation stays scattered across documents and tools, out of reach of AI How components, systems, and their dependencies could be represented in a way that works across all of these instances, and support AI-assisted inference about missing information or the consequences of change Whether ontologies, knowledge graphs, graph databases, multi taxonomy databases, or hybrid approaches, together with the AI methods to build and query them, are the right fit, and where each may fall short How AI could help turn design and operational data from many vessels into a Annuncio in inglese. Fonte