PhD position

PhD position IBM Zurich Research Laboratory Workplace Zurich - Zurich region - Switzerland Category Computer Science | Microtechnics Position Junior Researcher / PhD Position Published 15 December 2025 PhD position Foundation Models and Algorithms for Physics & Engineering Simulations, with Application to Assessment and Monitoring Ref. 2025_039 The IBM Research Zurich Laboratory together with ETH Zurich, offers an exciting opportunity for a motivated student to join our teams as a Doctoral Candidate (DC) under the DIAMOND EU Project, supported by the Marie Sklodowska- Curie Actions programme. You will work in a unique research-industry environment to develop novel algorithms and foundation models that accelerate computer simulations of complex physical and engineering systems, with applications in assessment and monitoring. Project Context Many natural and engineered phenomena are governed by complex differential equations. Traditional solvers require significant domain expertise, and can only handle these problems on the world’s largest supercomputers--expensive resources typically reserved for a small number of applications. Even then, a single simulation may take weeks to complete, and its results cannot be directly reused. AI-driven methods are changing this paradigm. By learning the underlying physics from simulation data, AI can build surrogate models that deliver high-fidelity results in seconds, enabling faster innovation across science and engineering. As reference, the candidate can review the following articles: - Blog: IBM Research is opening a new era in algorithmic development - Neural operators as a PDE solution paradigm: https://arxiv.org/abs/2108.08481') - Breakthrough approach in the car domain: https://arxiv.org/abs/2309.00583') - Recent SOTA work on Neural Operators: Transolver GitHub and Arxiv article Project Description and Objectives We expect the doctoral project to target some of the following objectives: - Develop new scalable and efficient algorithms and foundation models applicable across a range of physical and engineering domains, including civil infrastructure, assessment and asset monitoring, with a focus on quality of results and generalization across domains and applications; - Enable these algorithms to work with diverse data sources, including dense results calculated by computer simulations, partial/incomplete images, as well as sparse measures obtained from sensors installed on, e.g., robot dogs; - Design lightweights implementation of such foundation models and algorithms, so that they can be potentially run on edge devices (e.g., a robot dog); - Demonstrate the value of such models and algorithms on real world applications The exact workplan, methods and area of research will be defined together during the PhD, based on data availability, intermediate results, your interests, and new approaches discovered in the literature that may have high impact on the topic. Why Join Us? As part of our team, you will: - collaborate closely with experienced IBM Research scientists and software engineers, as well as ETHZ researchers who will support in tackling ambitious research challenges, - have access to modern GPUs and cloud infrastructure to accelerate your research, - be encouraged and supported to file your first patent with us and to publish your work in top AI conferences, building a strong foundation for both academic and industry careers, - work in a team that values collaboration, openness, and professional development. Location & Eligibility The candidate will be hired by the IBM Research Zurich Laboratory in the Frontiers of Computing and Simulation team led by Dr. Cristiano Malossi (IBM) . The candidate will be enrolled in the PhD program of ETH under the supervision of Prof. Michele Magno (ETH) . Minimum Qualifications - Strong academic track record, with background in Computing, Machine Learning, Mathematics, Statistics, or equivalent fields - Robust programming skills with Python - Proficiency with Unix/Linux environments - Solid communication and presentation skills in English - Willingness to learn, curiosity and growth mindset - Team-oriented, self-motivated, and able to solve problems independently Preferred Qualifications - Experience with one or more of the following: Physics-Informed Neural Networks (PINNs), Neural Operators / Neural Solvers, Algorithm development, Distributed computing, data structures, test automation, or CI/CD - Hands-on experience with PyTorch - Advanced programming skills (e.g., C/C++ or CUDA) - Ability to work independently and to adapt to changes in a fast-paced, evolving research environment The above listed qualifications are intended to give you an indication of what we are looking for. If you are excited about this role and you think you are the person we are looking for, even if you don’t meet every single requirement, we still encourage you to apply. Conditions to apply The researcher must not have resided or carried out their main activity (work, studies, etc.) in the country of the host organization (Switzerland) for more than 12 months in the 3 years immediately prior to the start date of the PhD. Diversity IBM is committed to diversity at the workplace. With us you will find an open, multicultural environment. Excellent flexible working arrangements enable all genders to strike the desired balance between their professional development and their personal lives. How to apply If you are interested in this exciting position, please submit your most recent curriculum vitae. We encourage candidates to also share a 3-minute video, in which they introduce themselves, as well as highlight their motivation and expertise. This video is entirely optional and not submitting one will not disadvantage your application. Interview process After the initial screening based on the uploaded documentation, identified candidates will be contacted for a first technical discussion on their experience, background, and motivations, followed by coding interview and an AI/ML interview. Further selection steps might be added based on candidates skills and project needs. Footer links More information In your application, please refer to myScience.ch and referenceJobID68918.