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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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partner, POST Luxembourg , one of the country's largest employers and a leading provider of postal, telecom, ICT, and financial services. The successful candidate will join the Computer Vision, Imaging, and
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partner, POST Luxembourg , one of the country's largest employers and a leading provider of postal, telecom, ICT, and financial services. The successful candidate will join the Computer Vision, Imaging, and
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partner, POST Luxembourg , one of the country's largest employers and a leading provider of postal, telecom, ICT, and financial services. The successful candidate will join the Computer Vision, Imaging, and
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partner, POST Luxembourg , one of the country's largest employers and a leading provider of postal, telecom, ICT, and financial services. The successful candidate will join the Computer Vision, Imaging, and
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currently lack reliable uncertainty estimates, limiting error detection and automation. The UMLFF project aims to develop next-generation MLFFs with built-in uncertainty predictions to enable safe, automated
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communication skills in English are mandatory Commitment, teamwork and a critical mind The ideal candidate should have experience in some of the following topics: Experience with control methods: impedance
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the Entrepreuneurship, Innovation, and New Technology (EINT) research group at SnT. The doctoral candidate will primarily be responsible for conducting information systems research using various qualitative methods (e.g
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of de-anonymization, including researching current de-anonymization strategies Investigate synthetic data generation methods and their utility Deploying and benchmarking the above-mentioned methods