The University of Luxembourg, a leading international research university, is offering a fully funded PhD position in the field of uncertainty-aware machine learning force fields (MLFFs) within the Faculty of Science, Technology and Medicine (FSTM). The position is part of the FNR-funded UMLFF project, which aims to advance atomistic simulations by developing next-generation MLFFs with built-in uncertainty predictions. This research is crucial for enabling safe, automated active learning and creating broad, reliable MLFFs that deliver quantum-chemical accuracy for large systems. As a doctoral researcher, you will join the Theoretical Chemical Physics group led by Professor Alexandre Tkatchenko and supervised by Dr. Igor Poltavskyi. The project is highly multidisciplinary, combining physics, chemistry, materials science, and machine learning. Your research will focus on uncertainty estimation in deep neural networks for MLFFs, implementing and testing uncertainty-aware loss functions, studying calibration and post-calibration for predictive uncertainty, and integrating uncertainty modules into MLFF architectures. You will also analyze MLFF feature spaces to detect out-of-distribution atomic environments, use chemical neighborhood representations to identify sparse or unseen cases, and combine ML features with chemical descriptors to enhance uncertainty robustness. Further tasks include building uncertainty-aware general purpose models, merging developments into unified MLFF architectures, training MLFFs on large, diverse datasets, and evaluating models through molecular dynamics simulations and benchmarks. You will integrate uncertainty-aware MLFFs into active learning frameworks, explore automated dataset generation for molecules and materials, and contribute to open-source tools for automated MLFF training. Applicants must have a Master's degree in Physics, Chemistry, Materials Science, Computer Science, or related fields, with a strong background in theoretical or computational physics or chemistry. Experience with machine learning, Python, and modern ML frameworks (PyTorch/JAX) is required. Desired skills include familiarity with atomistic simulation codes (ASE, FHI-aims, VASP, CP2K), knowledge of deep learning architectures, graph neural networks, or uncertainty quantification, and experience with HPC environments. B2-level proficiency in the language of the thesis is mandatory. The University of Luxembourg offers a modern, dynamic, and international environment with high-quality equipment and close ties to the business world and Luxembourg labour market. The yearly gross salary for the PhD position is EUR 41976 (full time). The contract is for 36 months, full-time, based at the Limpertsberg Campus. The university promotes an inclusive culture and encourages applications from individuals of all backgrounds. To apply, prepare a single PDF file including your CV, cover letter, transcript of university-level courses, and contact information for two references. Applications must be submitted online through the HR system; email applications will not be considered. Early application is encouraged as applications are processed upon reception.

The University of Luxembourg, a leading international research university, is seeking a postdoctoral researcher for the UMLFF project within the Faculty of Science, Technology and Medicine (FSTM). This project focuses on developing uncertainty-aware machine learning force fields (MLFFs) by integrating state-of-the-art equivariant neural network architectures with robust uncertainty estimates. The goal is to enable fully automated active learning in configurational and chemical space, advancing general-purpose MLFFs that are accurate and reliable across diverse chemical systems. The successful candidate will join the Theoretical Chemical Physics group, working closely with Dr. Igor Poltavskyi and Prof. Alexandre Tkatchenko, both recognized experts in machine-learning force-field development. The project benefits from access to high-performance computing resources, including the MeluXina supercomputer, and strong international collaborations with institutions such as the Max Planck Institute, Google DeepMind, and TU Berlin. Key scientific themes include intrinsic uncertainty estimation in MLFFs (epistemic and aleatoric), negative log-likelihood and calibration methods for force-field training, feature-space and orbit-based analysis of atomic environments, detection of extrapolation and low-reference data regimes, active learning in configurational and chemical space, and training and benchmarking of large-scale foundational MLFF models. The postdoctoral researcher will develop and implement uncertainty-aware MLFF architectures, design and test loss functions and calibration methods, analyze feature-space structure and model applicability domains, train and benchmark MLFF models on diverse molecular and materials datasets, integrate uncertainty estimates into active learning pipelines, and publish results in leading journals and conferences. Applicants must hold a PhD in physics, chemistry, materials science, computational science, or a related field, with a strong background in machine learning and/or computational chemistry. Experience with deep neural networks, scientific programming in Python, molecular simulations, force fields, or atomistic modeling is essential. Highly desirable qualifications include experience with equivariant or graph neural networks (e.g., NequIP, MACE, SO3LR, Allegro), MLFF training and validation, knowledge of uncertainty estimation (Bayesian or ensemble methods), experience with HPC environments and large-scale model training, and familiarity with DFT codes (e.g., FHI-aims) and molecular dynamics. The University of Luxembourg offers a modern, dynamic, and multilingual environment, with high-quality equipment and close ties to the business world and Luxembourg labour market. The position is a fixed-term contract for 12 months, full-time, with a yearly gross salary of EUR 85176. Applications should be submitted online through the HR system, including a CV with publication list, cover letter, PhD diploma or expected defense date, transcript of university-level courses, and contact details of 2-3 referees. Early applications are encouraged as review begins upon receipt. Applications by email will not be considered. The university promotes an inclusive culture and encourages applications from individuals of all backgrounds.