Research Assistant (m/f/d) with a Ph.D. in Civil Engineering, Engineering Physics, Physics, Mathematics, or a related field

Research Assistant (m/f/d) with a Ph.D. in Civil Engineering, Engineering Physics, Physics, Mathematics, or a related field

Berlin | Division 7.7: Modelling and Simulation Science / Research | Full-time / part-time possible | Temporary contract for 24 months | Salary group E 13 TVöD | Job-ID: J000000256

The Bundesanstalt für Materialforschung und -prüfung (BAM) is a materials research organization in Germany. Our mission is to ensure safety in technology and chemistry. We perform research and testing in materials science, materials engineering and chemistry to improve the safety of products and processes. At BAM we do research that matters. Our work covers a broad array of topics in the focus areas of energy, infrastructure, environment, materials, and chemistry and process engineering. We are looking for talented people to join us.

Responsibilities

• In this project, neural operators are combined with classical numerical methods to efficiently approximate parameterized finite-element models. By integrating data-driven and FEM-based approaches, hybrid models are developed that support real-time digital twins, uncertainty quantification, identification of model parameters from data, and probabilistic representations of residual model discrepancies.
• Development and implementation of FE models in FEniCSx and integration into neural operators and physics-informed neural networks (PINNs).
• Validation and uncertainty quantification of these surrogate models.
• Calibration of model parameters from experimental and operational data under uncertainty using these metamodels.
• Development of data-driven approaches for modeling residual prediction uncertainties and model-form errors.
• Documentation of the developed methods.
• Preparation of scientific publications and contribution to research proposals.
• Presentation of results at scientific conferences.

Qualifications

• A completed university degree (Diploma / Master's) and a Ph.D. in civil engineering, engineering physics, physics, mathematics, or a comparable field of study.
• Expertise in the development of FEM applications using programmable finite-element frameworks (e.g., FEniCSx), including e.g. implementation of constitutive models, element formulations or numerical solution procedures.
• Strong scientific programming skills, preferably in Python, and experience with modern software-development practices.
• Experience in implementing machine-learning and deep-learning methods.
• Excellent spoken and written English, as well as strong presentation and publication skills.
• Experience with scientific machine-learning approaches, particularly PINNs and neural operators (e.g., DeepONet, Fourier Neural Operators).
• Knowledge of uncertainty quantification, inverse problems, Bayesian inference, or parameter identification.

Additional Attributes

• A high degree of initiative and commitment.
• Ability to work in a team and willingness to cooperate.
• A goal-oriented and structured approach to work.
• Willingness and ability to make decisions.
• Good communication and information management skills.

Benefits

• Work in national and international networks with universities, research institutions, and industrial companies.
• Diverse tasks in a dynamic and future-oriented market at the interface between science, business, and politics.
• Certified family-friendly working environment.
• Attractive and modern working environment with excellent infrastructure and state-of-the-art scientific equipment (laboratories, BAM Data Store, high-performance computing, etc.).
• 30 vacation days per year based on a 5-day workweek, plus the 24th and 31st of December off.
• Mobile working.
• A responsible, interesting, and varied job in a professional and collegial environment.
• Work-life balance.

Professional Development Opportunities

We offer: Access to high-quality continuing education and training on digitalization topics such as programming, research data management, AI applications, and machine computing in science.

Networking and exchange in our interdisciplinary digitalization communities and at BAM digitalization events to develop innovative solutions together with our experts.

Targeted acquisition of skills in key technologies for laboratory digitalization - modern programming paradigms, connection to the electronic laboratory notebook, and development of metadata formats and ontologies - through practical learning formats such as workshops, online courses, and collaborative training with bundled knowledge sources.

Opportunity to actively participate in shaping BAM digitization projects and to contribute your own ideas for digital innovations.

Equal Opportunity and Diversity Statement

BAM promotes professional equality between women and men.

We therefore particularly welcome applications from women.

At the same time, we strive to reflect social diversity.

Every application is therefore welcome, regardless of gender, cultural or social background, religion, ideology or sexual identity.

In addition, BAM has set itself the goal of promoting the professional participation of people with severe disabilities.

The fulfillment of the job requirements is considered on an individual basis.

Severely disabled persons or persons of equal status will be given preferential consideration if they are equally qualified.

The advertised position requires a low level of physical aptitude.

Contact

Contact Dr. Jörg F. Unger Fachlicher Ansprechpartner Unter den Eichen 87 12205 Berlin joerg.unger@bam.de