This tender is from the country of Belgium in Europe region. The tender was published by INSTYTUT FIZYKI POLSKIEJ AKADEMII NAUK on 05 May 2025 for Symbolic artificial intelligence for hidden topological orders in quantum physics Artificial intelligence AI offers novel methodologies to unravel complex physical phenomena However most machine learning models lack transparency in their decision-making processes In this proposal we aim to develop a symbolic AI as a tool to reveal hidden topological orders in quantum physics To this end an AI-assisted symbolic regression method will be studied We focus on three main objectives i machine learning topological phases with experimental data ii uncovering hidden non-local symmetry-protected topological orders and iii searching for quantized topological invariants in an unsupervised fashion The interplay between symbolic AI and quantum physics is envisioned to bring new insights into topological phases Moreover the project will scrutinize the explainability and the robustness of machine learning models The investigations will provide concrete guidelines for accompanying theoretical and experimental studies at MagTop The outcomes of the project will pave the way to discover novel features of topological materials in a reliable and explainable way as well as provide great opportunities for me to reach a position of professional excellence and independence. The last date to submit your bid for this tender is 30 Jun 2026. This tender is for the companies specializing in Knotted nets, Artificial Intelligence and similar sectors.
Tender Organisation:
INSTYTUT FIZYKI POLSKIEJ AKADEMII NAUK
Tender Sector:
Tender Service:
Worldwide
Tender Region:
Tender Country:
Tender CPV:
39541210 : Knotted nets
72212514 : Artificial Intelligence
Tender Document Type:
Tender Notice
Tender Description:
Symbolic artificial intelligence for hidden topological orders in quantum physics Artificial intelligence AI offers novel methodologies to unravel complex physical phenomena However most machine learning models lack transparency in their decision-making processes In this proposal we aim to develop a symbolic AI as a tool to reveal hidden topological orders in quantum physics To this end an AI-assisted symbolic regression method will be studied We focus on three main objectives i machine learning topological phases with experimental data ii uncovering hidden non-local symmetry-protected topological orders and iii searching for quantized topological invariants in an unsupervised fashion The interplay between symbolic AI and quantum physics is envisioned to bring new insights into topological phases Moreover the project will scrutinize the explainability and the robustness of machine learning models The investigations will provide concrete guidelines for accompanying theoretical and experimental studies at MagTop The outcomes of the project will pave the way to discover novel features of topological materials in a reliable and explainable way as well as provide great opportunities for me to reach a position of professional excellence and independence
Tender Bidding Type:
Tender Notice No:
Tender Document:
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