Postdoc in Machine Learning and Surrogate Modelling

About us

The University of Luxembourg is an international research university with a distinctly multilingual and interdisciplinary character.

The Interdisciplinary Centre for Security, Reliability and Trust (SnT) at the University of Luxembourg is a leading international research and innovation centre in secure, reliable and trustworthy ICT systems and services.
We play an instrumental role in Europe by fueling innovation through research partnerships with industry, boosting R&D investments leading to economic growth, and attracting highly qualified talent. We look for researchers from diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular Networks, and ICT Services & Applications.

Your role

This position sits at the interface of machine learning, uncertainty quantification and computational biomechanics. You will work within the Legato group and in close collaboration with ARSPECTRA to design and deploy probabilistic surrogate models that run on ARSPECTRA's devices and provide robust, real-time guidance to neurosurgeons.

Key Responsabilities:

• Design and implement Bayesian graph neural networks and convolutional neural networks as surrogates for high-fidelity biomechanical models
• Quantify and propagate uncertainty, and develop strategies for model selection and switching in real time
• Integrate surrogate models with physics-based solvers, e.g. SOFA, FEniCSx, SOniCS, and clinical or phantom data
• Deploy models on ARSPECTRA hardware, including optimisation for embedded and GPU platforms.
• Collaborate with ARSPECTRA engineers and surgeons to create a complete AR guidance pipeline : tracking, SLAM, gaze, user interface

Your profile

• PhD in machine learning, computational mechanics, computer science, applied mathematics or similar
• Strong experience with deep learning, e.g. PyTorch, JAX, TensorFlow, and probabilistic methods
• Familiarity with graph neural networks, convolutional architectures and surrogate modelling for physical systems
• Solid understanding of PDE-based models or the motivation to acquire this knowledge
• Experience with real-time or edge deployment (CUDA, ONNX, TensorRT, FPGA) is an advantage
• Ability to work across disciplines - mechanics, ML, hardware, medicine

We offer

• Multilingual and international character. Modern institution with a personal atmosphere. Staff coming from 90 countries. Member of the "University of the Greater Region" (UniGR)
• A modern and dynamic university. High-quality equipment. Close ties to the business world and to the Luxembourg labour market. A unique urban site with excellent infrastructure
• A partner for society and industry. Cooperation with European institutions, innovative companies, the Financial Centre and with numerous non-academic partners such as ministries, local governments, associations, NGOs …

How to apply

Applications should include:

Early application is highly encouraged, as the applications will be processed upon reception. Please apply ONLINE formally through the HR system. Applications by Email will not be considered.

All qualified individuals are encouraged to apply. In line with our values, the University of Luxembourg promotes an inclusive culture. We encourage applications from individuals of all backgrounds and are dedicated to upholding equality and respect for our employees and students.

General information:

• Contract Type: Fixed Term Contract 12 Month
• Work Hours: Full Time 40.0 Hours per Week
• Location: Belval Campus
• Internal Title: Postdoctoral researcher
• Job Reference: UOL07986

The yearly gross salary for every Postdoctoral Researcher at the UL is EUR 85176 (full time).