You will contribute to COMPOSELECTOR H2020 project (www.composelector.net) goals in developing a Business Decision Support System (BDSS) to support the complex decision process involved in the selection and design of composite materials (CM).
COMPOSELECTOR has a strong focus on the integration and innovative development of a Multi-Disciplinary Optimization framework, which will allow for time, resources and costs saving while increasing performance and functionality.
Another key enabler of the approach proposed in COMPOSELECTOR is the ability to account for different class / aspects of uncertainty within models and manufacturing design processes.
With this regard, COMPOSELECTOR aims at :
Provide Uncertainty Analysis (UA) in order to quantify the influence of input parameters on an output of interest. Inputs include, for instance, the material microstructure, process conditions and potential expected performances
Develop Uncertainty Management strategies for the multi-model design of CMs
Implement Uncertainty Management (UM) techniques into single- and multi-model workflows
Introduce UM as a valuable metric in the design optimization
Solve technical complexities in software integration that include UM
As a postdoctoral researcher, you will join a talented, diverse team to make a scientific impact in controlling and predicting the behavior of complex engineered composite materials and structures using advanced discretization methods and parameter estimation methods, continuum modeling and optimization techniques.
The prospective candidate will participate in further designing of the BDSS by the integration of the developed tools and methodologies for uncertainty management in the MUPIF platform (https : / / github.
com / mupif / mupif) and engage in one or more of the following focus areas :
Development of optimization methods related to uncertainty in model parameterization
Development of uncertainty quantification methods and tools
Development of methods and tools to propagate uncertainty through predictive physics-based simulations
The selected candidate could also contribute if necessary to the development of techniques for efficient exploration of multi-
level design spaces using a Bayesian Network Classifier
Ph.D. in mathematics, computational mechanics, computer science, statistics or a closely related discipline
Minimum Job Requirements :
The successful applicant should have a strong background in at least one of the specialty areas of continuous optimization and uncertainty quantification
Strong programming skills in Python
Demonstrated ability to publish research in peer-reviewed journals or conference proceedings
Demonstrated ability to communicate research to an interdisciplinary audience and work on an interdisciplinary team
Desired Skills (not mandatory) :
Familiarity with using High Performance Compute clusters
Experience with version-controlled code projects