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About the position A three-year position as Postdoctoral Research Fellow inQuantum Machine Learning for Multimode Mechanics (QM3L / QuantERA 2025) is available at the Department of Physics.  Supervisors are Prof. Francesco Massel and Prof. Morten Hjorth-Jensen.  Starting date no later than January 1, 2027. No one can be appointed for more than one Postdoctoral Research Fellowship at the University of Oslo. Project description and work tasks The postdoctoral position is part of the QuantERA project ‘QM3L - Quantum protocols for Multimode Mechanics advanced by Machine Learning’, a European consortium combining theory, algorithms, and experiments on multimode mechanical quantum platforms (circuit quantum acoustodynamics with transmon–HBAR devices; HBAR arrays; microwave optomechanics). The postdoc will work at UiO with Massel and Hjorth-Jensen, focusing on theoretical and algorithmic developments that enable machine-learning-assisted discovery, control, and certification of complex multimode quantum states. The work will interface closely with experimental partners (ETH Zürich, Aalto University) and theory partners (TU Darmstadt, Palacký University). Main work tasks may include (depending on profile and project needs): Develop ML-assisted Hamiltonian discovery/engineering methods for multimode bosonic and hybrid quantum systems (including open-system and non-Hermitian effects). Design noise-resilient control and optimization strategies, including reinforcement learning (RL) and optimal control approaches for state preparation and measurement protocol design under hardware constraints. Develop open-source software and documented benchmarks. Co-author high-quality publications and contribute to consortium deliverables and reporting; participate in partner visits, workshops, and training activities. The main purpose of a postdoctoral fellowship is to provide the candidates with enhanced skills to pursue a scientific top position within or beyond academia. To promote a strategic career path, all postdoctoral research fellows are required to submit a  professional development plan no later than one month after commencement of the postdoctoral period. What skills are important in this role? The Faculty of Mathematics and Natural Sciences has a strategic ambition to be among Europe’s leading communities for research, education and innovation. Candidates for these fellowships will be selected in accordance with this, and expected to be in the upper segment of their class with respect to academic credentials. Required qualifications: A degree equivalent to a Norwegian doctoral degree in physics, applied mathematics, computer science, or a closely related area. If the degree is not yet formally received, the doctoral dissertation must be submitted for evaluation by the application date. Only applicants with an approved doctoral thesis and public defence are eligible for appointment. Strong background in at least two of the following: quantum optics / quantum information / open quantum systems quantum control / optimal control machine learning (including deep learning and/or reinforcement learning) numerical simulation of quantum dynamics Proven programming skills (e.g., Python/Julia/C++), including experience with scientific computing tools Ability to communicate research results clearly in written and oral English Desired qualifications: Experience with reinforcement learning for physical control problems and/or sample-efficient optimization Familiarity with continuous-variable/bosonic systems, non-Gaussian states, multimode entanglement, or Wigner-function methods Experience with HPC workflows (cluster computing, GPU computing, reproducible pipelines) Track record of collaborative work across theory/experiment or interdisciplinary teams All candidates and projects will have to undergo a check versus national export, sanctions and security regulations. Candidates m