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include: Intrinsic uncertainty estimation in MLFFs (epistemic & aleatoric uncertainty) Negative log-likelihood and calibration methods for force-field training Feature-space and orbit-based analysis
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activities, including the design and implementation of novel methods, experimental evaluation, and dissemination of results through high-quality scientific publications. Beyond technical contributions, you are
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genomic graphs. The project will also deliver efficient algorithms to train these models under budget and time constraints, facilitating flexible adoption of the methods. The project is carried out in close
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empirical methods/mixed methods research Openness to further research in participatory, arts-based and or digitally-informed contexts in the area of inclusive education International publications in peer
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validation (V&V) techniques for space systems, software and algorithms with a focus on specific challenges of space-borne perception and proximity operations uncooperative spacecraft . Develop novel methods
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heterogeneous multi-omics datasets. Integrative Data Analysis: Perform and lead analysis of large-scale multi-omics datasets, including RNA/DNA sequencing, methylation, and metabolomics. Method Development
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, generating synthetic training datasets and extracting robust features for SIGINT and spectrum‑monitoring pipelines Develop and integrate TDOA/FDOA‑based geolocation methods into RF situational‑awareness
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a tax haven. By employing a law and history approach the LUXTAX project aims to investigate the history of Luxembourg's tax regime, its beneficial elements through time as well as the socio-economic
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methods and tools for adaptive, AI-driven embedded algorithms to support real-time analytics and decision-making in IoT, wireless communication, and remote sensing systems Collaborate with multidisciplinary
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to: Develop mechanistic and data-driven models to analyse concentration–response relationships and biological readouts from advanced in vitro assays Apply statistical and computational methods to quantify