{"id":3682,"date":"2024-08-07T19:16:39","date_gmt":"2024-08-07T17:16:39","guid":{"rendered":"https:\/\/www.cyberagentur.de\/tenders\/robust-and-secure-machine-learning-rsml-research-and-development-of-approaches-to-robust-and-secure-machine-learning-for-security-and-defense-relevant-operational-systems\/"},"modified":"2026-04-28T11:07:59","modified_gmt":"2026-04-28T09:07:59","slug":"robust-and-secure-machine-learning-rsml-research-and-development-of-approaches-to-robust-and-secure-machine-learning-for-security-and-defense-relevant-operational-systems","status":"publish","type":"tender","link":"https:\/\/www.cyberagentur.de\/en\/tenders\/robust-and-secure-machine-learning-rsml-research-and-development-of-approaches-to-robust-and-secure-machine-learning-for-security-and-defense-relevant-operational-systems\/","title":{"rendered":"Robust and Secure Machine Learning (RSML); research and development of approaches to robust and secure machine learning for security and defense-related operational systems"},"content":{"rendered":"  <ol class='ce-item-info property-list property-list--small'>\n    <li class='property'>\n    <span class='property__name'>Status:<\/span>\n    <span class='property__value'>Project phase<\/span>\n<\/li>\n  <\/ol>\n<h3 class=\"wp-block-heading has-text-align-left\">Description<\/h3>\n\n<p>The aim of the competition is to research and develop new approaches to increase the robustness and security of neural AI approaches in the context of application systems in the domains of internal and external security. The research and development activities should address the question of whether, how and to what extent safety and robustness properties can be demonstrated for specific application domains and systems, where the limits of verifiability lie and how systems with such properties can be realized as prototypes or minimal viable products. Measured in terms of technology readiness level (TRL), this project will pursue approaches with TRL 1 (basic research) to TRL 4 (laboratory prototypes).  <\/p>\n\n<p>The main objective of the procurement is to demonstrate and evaluate how the approaches developed to increase the robustness and security of neural AI approaches can be demonstrably safely applied along the entire ML process chain using a real application system for internal or external security at the end of the project term. This can be done with the help of software artifacts such as validation and data preparation tools, model architectures, foundation models and integrated ML systems, which must prove themselves in laboratory and application tests as well as evaluations against competing approaches and possibly baseline models.   Thematically, the project is divided into the following main research areas: 1) Automated data quality assurance, 2) Hybrid models from neural and symbolic AI systems, 3) Formal verification of ML models, 4) Secure system embedding, 5) Verification over the entire life cycle. The service provision is divided into several phases.      The best and most innovative of the competing solutions will be identified and selected in stages as part of the procedure on the basis of the criteria set out in the tender documents.<\/p>\n\n<h3 class=\"wp-block-heading has-text-align-left\">Deadline for the submission of requests to participate<\/h3>\n\n<p><strong>23.05.2023, 18:00,<\/strong> via <a href=\"http:\/\/www.evergabe-online.de\/\" target=\"_blank\" rel=\"noreferrer noopener\">www.evergabe-online.de<\/a> or directly  <a href=\"https:\/\/www.evergabe-online.de\/tenderdetails.html?id=505660\" target=\"_blank\" rel=\"noreferrer noopener\">www.evergabe-online.de\/tenderdetails.html?id=505660 <\/a><\/p>\n\n<p><strong>Client: <\/strong>Agentur f\u00fcr Innovation in der Cybersicherheit GmbH<br\/><strong>Awarding body: <\/strong>Agentur f\u00fcr Innovation in der Cybersicherheit GmbH<br\/><strong>Services and products:<\/strong> Research and development services and related consulting<br\/><strong>Tender scope:<\/strong> Europe-wide<br\/><strong>Procurement procedure:<\/strong> Pre-commercial procurement<br\/><strong>Type of award:<\/strong> based on a negotiated procedure with prior call for competition<br\/><strong>Place of fulfillment:<\/strong> Halle (Saale), district-free city<br\/><strong>Contact:<\/strong> exclusively via the bidder portal<br\/><strong>Contract notice number: <\/strong>2023\/S 070-206022<br\/><strong>CPV code:<\/strong> 73000000<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Description The aim of the competition is to research and develop new approaches to increase the robustness and security of neural AI approaches in the context of application systems in the domains of internal and external security. The research and development activities should address the question of whether, how and to what extent safety and [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","class_list":["post-3682","tender","type-tender","status-publish","hentry"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.5 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Robust and Secure Machine Learning (RSML); research and development of approaches to robust and secure machine learning for security and defense-related operational systems - Cyberagentur<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.cyberagentur.de\/en\/tenders\/robust-and-secure-machine-learning-rsml-research-and-development-of-approaches-to-robust-and-secure-machine-learning-for-security-and-defense-relevant-operational-systems\/\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Robust and Secure Machine Learning (RSML); research and development of approaches to robust and secure machine learning for security and defense-related operational systems - Cyberagentur\" \/>\n<meta property=\"og:description\" content=\"Description The aim of the competition is to research and develop new approaches to increase the robustness and security of neural AI approaches in the context of application systems in the domains of internal and external security. 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