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We are hiring

To develop solutions to the pressing challenges we face in integrating data and models into self-learning systems that enable the intelligent design, optimization and control of products, processes and systems, we are looking for 15 highly motivated Early Stage Researchers (ESRs). To tackle these challenges, GREYDIENT will train its ESR’s in a wide spectrum of fields, including the modelling, propagation and quantification of the relevant variabilities, the application of big data and machine learning methods, as well as the optimal combination of data-driven approaches with numerical models.

All our ESR’s will obtain a PhD from an internationally respected university, build experience in communicating and disseminating their work, applying their research skills in a non-academic context and receive in-depth training in transferable skills such as commercialization, collaboration and entrepreneurship. Note that strict eligibility criteria are applicable; applications of applicants who are not eligible will not be considered.

Eligibility Criteria

Available Positions

ESR 1 - KU Leuven

Thermal modelling and online heat transfer estimation using Active Learning grey-box approaches

 

Objectives: (1) To construct a production-process virtual twin consisting of an ensemble of data-driven black box approaches, surrogate models and full-scale, non-linear, finite-element computations, (2) to apply active learning to train this virtual twin based on the full-scale white box model, (3) to apply this virtual twin in combination with active learning to create an optimal sensor lay-out, (4) to apply the developed methodology on a projection welding machine at KU Leuven.

 

Expected results: A generic methodology to optimize large-scale sensor arrays in production plants, as well as perform online process control, via an optimal combination of simulation models with experimental data, validated on the projection welding process.

 

Applicant profile: Master of science in Mechanical Engineering or Computer Science or a related field, ideally with background in numerical analysis of structures or processes. Team player. Excellent communication skills (written and oral) in English.

 

PhD location: The recruited ESR will perform their research in the Reliable and Robust (R2D) group at KU Leuven for a period of 3-4 years. They will also be able to conduct parts of their research at ETH Zürich and perform industrial validation of their developments at Kapernikov.

ESR 2 - KU Leuven

Digital Image Correlation enriched quantification of polymorphic random field uncertainty in finite-element models

 

Objectives: (1) to develop a methodology to simulate strain fields based on imprecise random-field valued material parameters using a Digital Image Correlation simulator, (2) to apply non-linear dimension-reduction approaches to deduce a low-dimensional manifold from experimental strain fields, (3) to train a physics-informed deep-learning architecture to predict imprecise random-field descriptors including expert knowledge (4) validation of the inverse methodology on functionally graded materials.

 

Expected results: A methodology to inversely quantify non-homogeneous random-field uncertainty based on full-field strain measurements. Illustration of the method on the identification of functionally graded materials (FGMs).

 

Applicant profile: Master of science in Mechanical Engineering, Applied Mathematics or Computer Science or a related field, ideally with background in numerical analysis of structures or processes or numerical mechanics. Team player. Excellent communication skills (written and oral) in English.

 

PhD location: The recruited ESR will perform their research in the Reliable and Robust (R2D) group at KU Leuven for a period of 3-4 years. They will also be able to conduct parts of their research at Politecnico di Milano and Aristotle University of Thessaloniki and perform industrial validation of their developments at MatchID.

ESR 3 - Leibniz University Hannover

Efficient fuzzy analysis for real-time noise and vibration analysis

 

Objectives: (1) to expand the concept of intervening variables towards explicit dynamic FE analysis, (2) to combine this approach with series-expansion methods to solve the fuzzy-propagation problem, (3) to apply these methods to a practical car-crash analysis.

 

Expected results: Algorithms for real-time characterization of imprecise dynamical response, applied to transient dynamic analysis in the context of noise and vibration analysis.

 

Applicant profile: Master of science in Engineering, Applied Mathematics or Computer Science or a related field, ideally with background in numerical analysis of structures or processes and/or uncertainty quantification. Team player. Excellent communication skills (written and oral) in English.

 

PhD location: The recruited ESR will perform their research in the Institute for Risk and Reliability at the University of Hannover for a period of 3-4 years. They will also be able to conduct parts of their research at KU Leuven and perform industrial validation of their developments at SCALE.

ESR 4 - Leibniz University Hannover

Fast imprecise surrogate model for medium-fidelity data analysis

 

Objectives: Objectives: (1) to develop a highly efficient and effective surrogate model to derive predictions on the structural behaviour from a mix of medium-fidelity data (2) to benchmark the Interval Neural Network with respect to other set-valued surrogate models such as interval predictor models or probabilistic Neural Networks. (3) To apply Interval Neural Networks in an OpenCOSSAN implementation on a practical car-crash analysis in collaboration with SCALE.

 

Expected results: Algorithms for fast data analysis and decision support.

 

Applicant profile: Master of science in Engineering, Applied Mathematics or Computer Science or a related field, ideally with background in numerical analysis of structures or processes, machine learning and/or uncertainty quantification. Team player. Excellent communication skills (written and oral) in English.

 

PhD location: The recruited ESR will perform their research in the Institute for Risk and Reliability at the University of Hannover for a period of 3-4 years. They will also be able to conduct parts of their research at TU Munich and Polictecnico di Milano and perform industrial validation of their developments at SCALE.

ESR 5 - Politecnico di Milano

Advanced probabilistic black-box modelling methods for reliability, risk and resilience analysis of smart critical systems

 

Objectives: To develop a simulation framework for smart critical system resilience analysis that (1) identifies component contribution to the overall system risk and (2) prioritizes system improvement decisions for accident recovery, can (3) account for inter-dependence in the system’s components and (4) a wide range of both small and large uncertainties.

 

Expected results: A set of tools comprising advanced AI black-box models and advanced Monte Carlo simulation for the reliability, risk and resilience analysis of smart critical systems and infrastructures.

 

Applicant profile: Master of science in Engineering, Applied Mathematics or Computer Science or a related field, ideally with background in numerical analysis of structures or processes, machine learning and/or uncertainty quantification. Team player. Excellent communication skills (written and oral) in English.

 

PhD location: The recruited ESR will perform their research at Politecnico di Milano for a period of 3-4 years. They will also be able to conduct parts of their research at Leibniz University Hannover and perform industrial validation of their developments at Aramis.

ESR 6 - Politecnico di Milano

Multi-fidelity grey-box models for integrated, condition-informed, dynamic risk assessment

 

Objectives: (1) to develop dynamic modelling approaches capable of modelling both hard (physical) and logic (cyber) systems failures, (2) to construct a multi-fidelity grey-box model for dynamic reliability assessment of systems and (3) to adopt a Value of Information (VoI) approach to identify the most appropriate multi-fidelity grey-box model, (4) to validate the developments for a realistic case study in collaboration with Aramis.

 

Expected results: A validated dynamic multi-fidelity grey-box approach for risk assessment based on a VoI approach, including physical and cyber-systems failures.

 

Applicant profile: Master of science in Engineering, Applied Mathematics or Computer Science or a related field, ideally with background in numerical analysis of systems or processes, machine learning and/or uncertainty quantification. Team player. Excellent communication skills (written and oral) in English.

 

PhD location: The recruited ESR will perform their research at Politecnico di Milano for a period of 3-4 years. They will also be able to conduct parts of their research at Sigma-Clermont and perform industrial validation of their developments at Aramis.

ESR 7 - Aramis

Optimization of Energy Networks Equipped with Prognostics and Health Management capabilities

 

Objectives: (1) development of a methodology to estimate the reliability and availability of energy-grid systems including components equipped with Prognostics and Health Management (PHM) capabilities, (2) to estimate the reliability and availability of energy grid systems including components equipped with Prognostics and Health Management (PHM) capabilities (based on (1)) and (3) to apply the developed methods to obtain optimal solutions depending on the O&M costs as well.

 

Expected results: A set of methodologies and algorithms that have to potential to significantly improve the existing approaches for energy-grid optimization.

 

Applicant profile: Master of science in Engineering, Applied Mathematics or Computer Science or a related field, ideally with background in numerical analysis of systems or processes, machine learning and/or uncertainty quantification. Team player. Excellent communication skills (written and oral) in English.

 

PhD location: The recruited ESR will perform their research at Aramis for a period of 3-4 years. They will also be able to conduct parts of their research at Sigma-Clermont.

ESR 8 - TU Münich

A Learning-based Flexibility Approach for Lack-of-Knowledge Robustness in Early-Phase Crashworthiness Design

 

Objectives: (1) to derive artificial intelligence (AI) approaches for adaptively maximizing the flexibility of car-crashworthiness design based on low-fidelity component-level models, (2) to use AI to link experimental data and high-fidelity FE models to enhance the representation of mechanical characteristics provided by low-fidelity models, (3) to apply these methods at a practical case in collaboration with SCALE GmbH.

 

Expected results: Extension of the Solution Space Method (SSM) to derive optimized crash structures to account for lack-of-knowledge and adapt successively via machine-learning methods. Given the power of machine-learning capabilities, more complex crash load cases can be addressed in the future to enable an efficient and tailored virtual product development where experimental or computational data can be integrated.

 

Applicant profile: Master of science in Engineering, Applied Mathematics or Computer Science or a related field, ideally with background in transient dynamical numerical analysis of structures and components, machine learning and/or uncertainty quantification. Team player. Excellent communication skills (written and oral) in English.

 

PhD location: The recruited ESR will perform their research at TU Munich for a period of 3-4 years. They will also be able to conduct parts of their research at ETH Zurich and perform industrial validation of their developments at SCALE GmbH.

ESR 9 - BMW

Grey-box Approach for Verification & Validation (V&V) for Crashworthiness - Learning-based validation of crash models

 

Objectives: (1) To derive artificial intelligence (AI) methods to allow validation for crash cases when very limited or even no repetition of physical tests is possible, (2) to derive grey-box approaches to support the engineer improving and interpreting numerical simulations for crashworthiness by identifying correlations, weaknesses, and shortcomings of the virtual models, (3) application of these tasks at a practical case at BMW including Digital Image Correlation.

 

Expected results: As a result of the proposed developments, a new approach for Verification & Validation will be established where no stochastic physical experiments are required, and better and more confirmed evidence of the virtual models is achieved.

 

Applicant profile: Master of science in Engineering, Applied Mathematics or Computer Science or a related field, ideally with background in transient dynamical numerical analysis of structures and components, machine learning and/or uncertainty quantification. Team player. Excellent communication skills (written and oral) in English.

 

PhD location: The recruited ESR will perform their research at BMW for a period of 3-4 years. They will also be able to conduct parts of their research at Sigma-Clermont and KU Leuven.

ESR 10 - Sigma Clermont

Learning multivariate time series – Application to tire manufacturing and wear prediction

 

Objectives: (1) to develop an approach to learn time-univariate time series, (2) to extend this method to the learning of cross-dependent multivariate time series, (3) to apply this method to industrial problems in collaboration with Michelin.

 

Expected results: General method able to learn multivariate time series from data with deep neural network or other suitable machine learning techniques. Focus on time-series forecasting. Potential application to the inference of unobserved time series. Representative examples in tire industry.

 

Applicant profile: Master of science in Engineering, Applied Mathematics or Computer Science or a related field, ideally with background in numerical analysis of systems or processes, machine learning and/or uncertainty quantification. Team player. Excellent communication skills (written and oral) in English.

 

PhD location: The recruited ESR will perform their research at Sigma-Clermont for a period of 3 years. They will also be able to conduct parts of their research at Aristotle University of Thessaloniki and perform industrial validation of their developments at Michelin.

ESR 11 - Sigma Clermont

The use of machine learning and grey-box models to solve complex time-consuming RBDO problems

 

Objectives: (1) to develop an efficient approach for reliability estimation based on machine-learning separators, (2) to apply this method in a multi-fidelity modelling context, (3) to apply zero-order optimization algorithms to solve a reliability-based design-optimization problem based on machine-learning separators and (4) to apply these developments to a realistic case study of a production process in collaboration with Radiall, a producer of electric connectors for automotive industry.

 

Expected results: Efficient and new methodology for RBDO problems based on machine learning separators, multi-fidelity modelling and zero-order optimization algorithms to efficiently perform a reliability-based design-optimization of production processes.

 

Applicant profile: Master of science in Engineering, Applied Mathematics or Computer Science or a related field, ideally with background in numerical analysis of systems or processes, machine learning and/or uncertainty quantification. Team player. Excellent communication skills (written and oral) in English.

 

PhD location: The recruited ESR will perform their research at Sigma-Clermont for a period of 3 years. They will also be able to conduct parts of their research at ETH Zurich and perform industrial validation of their developments at Radiall.

ESR 12 - Aristotle University of Thessaloniki

Random-field modelling of the mechanical properties of composite materials at different scales

 

Objectives: (1) To reconstruct the morphology of random microstructures based on computer-simulated or SEM images, (2) parameter identification of random fields describing the spatial variability of mechanical properties of composites at different scales, (3) Investigation of the effect of the probabilistic characteristics of the input random fields on the response variability of composite structures using stochastic finite elements, (4) application of the methodology to design reliable composite materials with the desired macroscopic properties based on a tuned microstructure.

 

Expected results: A generic approach to identify and reconstruct random fields on mechanical properties, as well as to assess the effect of spatial variability on structural reliability of composite components, based on a numerical model of the structure, advanced probabilistic techniques and microstructure reconstruction from SEM images.

 

Applicant profile: Master of science in Engineering, Computational Mechanics or Applied Mathematics or a related field, ideally with background in numerical analysis of systems or processes, machine learning and/or uncertainty quantification. Team player. Excellent communication skills (written and oral) in English.

 

PhD location: The recruited ESR will perform their research at Aristotle University of Thessalonilki for a period of 3-4 years. They will also be able to conduct parts of their research at Sigma-Clermont and perform industrial validation of their developments at Beta CAE systems.

ESR 13 - ETH Zürich

Active-learning multi-fidelity grey-box modelling

 

Objectives: (1) to construct multi-fidelity models based on models with different levels of discretization, linearization, etc.; (2) to use active learning to optimally distribute the computational budget among different fidelity levels; (3) to include data-driven approaches in the multi-fidelity framework and use active learning to select from the combination of white-box, data-driven models; (4) to validate the developed methodologies on a case study at EDF.

 

Expected results: An algorithm to optimally distribute the available computational/financial resources between different levels of modelling fidelity and different surrogate modelling strategies will be implemented. The resulting strategy is expected to provide a significant improvement on the feasibility of complex uncertainty quantification analyses for expensive grey-box-type models.

 

Applicant profile: Master of science in Engineering, Applied Mathematics or Computer Science or a related field, ideally with background in numerical analysis of systems or processes, machine learning and/or uncertainty quantification. Team player. Excellent communication skills (written and oral) in English.

 

PhD location: The recruited ESR will perform their research at ETH Zurich for a period of 3-4 years. They will also be able to conduct parts of their research at TU Munich and perform industrial validation of their developments at EDF France.

ESR 14 - ETH Zürich

Uncertainty quantification and reliability analysis for noisy grey-box models

 

Objectives: (1) to re-formulate the reliability problem in the context of noisy grey-box models, (2) to apply sparse polynomial chaos expansions as a denoising tool (3) to combine denoising and active learning to obtain highly efficient reliability estimates for grey-box models based on expensive industrial models and noisy data.

 

Expected results: A method that provides reliable and cost-efficient estimates of the probability of failure of structures and systems that, due to employed grey-box modelling approaches, contain noise-contaminated limit-state functions (e.g., due to numerical or experimental noise).

 

Applicant profile: Master of science in Engineering, Applied Mathematics or Computer Science or a related field, ideally with background in numerical analysis of systems or processes, machine learning and/or uncertainty quantification. Team player. Excellent communication skills (written and oral) in English.

 

PhD location: The recruited ESR will perform their research at ETH Zurich for a period of 3-4 years. They will also be able to conduct parts of their research at KU Leuven and perform industrial validation of their developments at Phimeca.

ESR 15 - Phimeca

Machine learning and physical modelling interactions for vehicle batteries optimisation

 

Objectives: (1) To develop a multi-physics numerical model to represent battery degradation and performance, (2) to implement this model in combination with real monitoring data in a functional mock-up interface (FMI) framework, (3) to develop a method to iteratively update the numerical model via Bayesian model updating and (4) to validate this method on a real electric vehicle battery pack case.

 

Expected results: A method for efficiently and effectively monitoring battery degradation and performance using an adaptive grey-box virtual twin approach, based on a FMI integration of a multi-physical model with online monitoring data.

 

Applicant profile: Master of science in Engineering, Applied Mathematics or Computer Science or a related field, ideally with background in numerical analysis of systems or processes, machine learning and/or uncertainty quantification. Team player. Excellent communication skills (written and oral) in English.

 

PhD location: The recruited ESR will perform their research at Phimeca for a period of 3-4 years. They will also be able to conduct parts of their research at Sigma-Clermont, Politecnico di Milano and Leibniz University Hannover.

Interested in joining Greydient?

Apply for one of our open ESR positions by filling out the form below. Please add your CV and motivation letter to your application.

Deadline for all job applications is March 31st


    Please select up to 3 ESR positions you'd like to apply for (min. 1 required):



    Why do these positions appeal to you?

    Please give a list of countries where you have stayed for 12 consecutive months in the past 3 years

    How many years of scientific research experience since your first MSc diploma do you have?

    Please also give two references (full names and contact info):

    CV


    Motivation Letter


    Greydient Consortium

    Greydient brings together experts from academy and leading European companies: 6 countries, 7 universities and 11 industrial partners.