Huawei is a leading telecom solutions provider. Through continuous customer-centric innovation, Huawei has established end-to-end advantages in Telecom Network Infrastructure, Application & Software, Professional Services and Devices. With comprehensive strengths in wireline, wireless and IP technologies, Huawei has gained a leading position in the All-IP convergence age. Its products and solutions have been deployed in over 100 countries and have served 45 of the world's top 50 telecom operators, as well as one third of the world's population.

Internship in Nonlinear System Identification

Nonlinear system identification aims at identifying the mathematical model of a nonlinear system from measurement data of the system inputs and outputs. This method is often used to identify a part of a complex networked system. The identified model can then be involved in various network predictive tasks and procedures.

Unstructured or black-box models often rely on neural network models to exploit their nonlinear function approximation properties. On the other hand, structured or grey-box models are based on the interconnection of linear and nonlinear subsystems. Among the most common model structures, those of main consideration for this internship are the multiple-input multiple-output (MIMO) i) parallel-Wiener, and ii) parallel Wiener-Hammerstein (with and without feedback) models due to their universal approximation capability. Other the other hand, when a measurement of the input to the nonlinear map is available nonlinear identification with Hammerstein model structure is less difficult than identification with the Wiener structure. This observation has led to consider particular systems that are nonlinear in measurement data and linear in unmeasured states.

Objective: the goal is first to extend/combine existing structured modeling methods to Hammerstein-Wiener and parallel Hammerstein systems. Then by decomposing the dynamics such as to isolate the nonlinear part in between two linear subsystems further extend these methods for parallel Wiener-Hammerstein systems.

Task: Design and compare different subspace identification algorithms as well as basis function optimization algorithms. Evaluate and compare these algorithms both formally and numerically using various (synthetic or real) benchmark cases. Note that comparison against black box modeling methods is also be considered as part of the taskplan. These tasks will be realized under supervision of senior (postdoc-level) researcher.

Duration: from 6 months to 1 year (max.)

Candidate profile:

  • if MSc thesis: the candidate must be following the last year of the curriculum in, e.g., Applied/ Numerical mathematics, Math/Mechanical engineering, Theoretical computer science, Computer science engineering. Detailed coordinates of MSc promotor and his/her academic affiliation must be provided in the CV application form.
  • if internship: the candidate must have completed his MSc (in one of these disciplines). Copy of the MSc diploma/certificate shall be included in annex of the CV. The internship can also be considered as part of post-MSc graduation or PhD graduation program.
  • Good knowledge of networked systems is considered as a plus.

Note well: candidate must have obtained their University degree from an academic institution of one of the EU country.

Starting date: Jun.1st, 2021.

Your application will be evaluated by the HR department of Huawei R&D Sites in Belgium and the Netherlands itself. For any additional feedback regarding your application, we kindly refer you to the Huawei R&D Sites in Belgium and the Netherlands HR department.


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