PATTERN will create a long-lasting, multidisciplinary, academic-industrial network for doctoral training, with leading European industry and academia, to achieve a breakthrough in the design of innovative EMI solutions, throughout their lifecycle, with AI acting as the key enabler of a new design philosophy
For this inclusion to occur, each DC will develop through their research the missing dedicated components, tools and techniques, and apply them to a representative set of EMI solutions under development. This hands-on training is supplemented with several scientific professional courses and an immersive training where the DCs can fine-tune their skills for the Jobs of tomorrow, while addressing the societal challenges of the PATTERN program.
All applications proceed first through the on-line recruitment portal. Candidates apply electronically for one to maximum three positions and indicate their preference. Candidates provide all requested information including a detailed CV (Europass format obligatory), a motivation letter and transcripts of bachelor and master’s degree. During the registration, applicants will need to prove that they are eligible (mobility criteria, and English language proficiency). The deadline for the online registration is 30 Sept. 2024. In the event that the vacancies are not filled during the initial recruitment process, the positions will continue to be re-opened and managed by each of the Beneficiaries. PATTERN is dedicated to promoting the role of women in science, and, therefore, explicitly invites women to apply.
The PATTERN Recruitment Committee selects between 32 and maximum 48 candidates for the Recruitment Event which will take place Online (Mid-November 2024). The selected candidates provide a 20-minute presentation and are interviewed by the Recruitment Committee. Candidates will be asked about a given domain relevant peer-reviewed paper (prior to the recruitment event) to test their background/profile for the DC position.
Prior to the recruitment event, online interviews between the Supervisor(s) and the candidates are recommended, along with online personality tests.
The final decision will be communicated after the Recruitment Event (End-November 2024). The selected DCs are expected to start their research as quickly as possible in 2025.
- Supported researchers must be doctoral candidates, i.e. not already in possession of a doctoral degree at the date of the recruitment.
- Recruited researchers can be of any nationality and must comply with the following mobility rule: they must not have resided or carried out their main activity (work, studies, etc.) in the country of the recruiting beneficiary for more than 12 months in the 36 months immediately before their recruitment date. For ‘International European Research Organisations’ (IERO), ‘international organisations’, or entities created under Union law, the researchers must not have spent more than 12 months in the 36 months immediately before their recruitment in the same appointing organisation. Compulsory national service, short stays such as holidays and time spent by the researcher as part of a procedure for obtaining refugee status under the Geneva Convention are not considered.
The successful candidates will receive an attractive salary in accordance with the MSCA regulations for Doctoral Researchers. The nominal (gross) amount includes (at least) a living allowance (€3400 per month, country coefficient applies to allow for the difference in cost of living in different EU Member States), a mobility allowance (€600 per month), and a family allowance (€660 per month, if applicable). Deductions will apply for social security contributions and/or taxes according to the applicable national laws of the country were the recruiting organization is located.
The guaranteed PhD funding is for 36 months (i.e., EC funding, additional funding is possible, depending on the local Supervisor, and in accordance with the regular PhD time in the country of origin). Position DC16 is an exception to these conditions and will be directly funded by UKRI (Public Body for Innovation and Research in UK). In addition to their individual scientific projects, all fellows will benefit from further continuing education, which includes internships and secondments, a variety of training modules as well as transferable skills.
10 PhD positions
Host: TUe (NL)
Main supervisor: Dr. Ir. Anne Roc’h
Co-supervisors/mentors: Prof. Philippe Besnier (IETR (CNRS), FR)/ Ir. Rob Kleihorst (Philips, NL)/ Prof.dr.ir. Roland Toth (TUe, NL)
Duration: 36 months
Required profile: Electronic Engineering Desirable skills/interests: Electromagnetism, Electromagnetic Compatibility, Metrology, Artificial Intelligence.
Objectives: This DC first goal will be to unravel a structural understanding of the exchange of parasitic energy of cables with its environment using AI tools. The so-called EMNF (ElectroMagnetic Noise Footprint) comprises a set of characteristic curves obtained from stand-alone measurements on a device. A second goal will consist in exploring how to combine two or more EMNFs. The work will support an optimization (within the SSbD framework) of cable routing in a MedTech product by combining EMNFs.
Secondments (2-4 months in total): Safran (FR) and IETR (CNRS) (FR).
Host: UGENT (BE)
Main supervisor: Tom Dhaene (UGENT, BE)
Co-supervisors/mentors: Ivo Couckuyt (UGENT, BE), Dirk Deschrijver (UGENT, BE), Dr.ir Anne Roc’h (TUe, NL)
Duration: 36 months
Required profile: Electrical Engineering
Desirable skills/interests: Machine Learning, Electromagnetic Compatibility, MW/RF
Objectives: By leveraging state-of-the-art, data-efficient Machine Learning (DE-ML) approaches and developing novel Bayesian active learning (BAL) techniques, the goal is to reduce the time of standard well-known, lengthy EMC tests, especially for radiated field sampling.
Secondments (2-4 months in total): TUe (NL), Thales (NL)
Host: TUHH (DE)
Main supervisor: Prof. Christian Schuster (TUHH, DE)
Co-supervisors/mentors: Dr.ir Stanislav Kovar (TBU, CZ) / Prof. Matthias Mnich (TUHH, DE) / Nexperia (DE)
Duration: 36 months
Required profile: Electrical Engineering, Physics, or Data Science Desirable skills/interests: Electromagnetics, Electromagnetic Compatibilty, Machine Learning, Artificial Intelligence
Objectives: This DC project aims to extend the state of the art in application of methods of machine learning (ML) to the field of electronic computer aided design (ECAD). Specifically, it aims to develop, train, optimize and evaluate ML methods for simulation and optimization of high-speed packages for discrete devices in combination with generating and using an adaptive database. The main objective is to adapt ML methods which model and ultimately replace the existing design process in most steps and hierarchies.
Secondments (2-4 months in total): Nexperia (DE) and IETR (CNRS, FR).
Host: TUe (NL)
Main supervisor: Dr.ir Anne Roc’h (TUe, NL)
Co-supervisors/mentors: Prof. Frank Leferink (UT, NL)/ Ing. Libor Valíček (Schneider, CZ)/ Dr. Ir. Roland Toth (TUe, NL)
Duration: 36 months
Required profile: Electrical Engineering
Desirable skills/interests: Electromagnetism, Electromagnetic Compatibility, Power Electronics, Artificial Intelligence.
Objectives: This DC first goal will be to unravel a structural understanding of the exchange of parasitic energy of EMI Filters with its environment using AI tools. The so-called EMNF (ElectroMagnetic Noise Footprint) comprises a set of characteristic curves obtained from stand-alone measurements on a device. A second goal will consist in exploring how to combine two or more EMNFs. The work will support the optimization (within the SSbD framework) of EMI filters within products by combining EMNFs.
Secondments (2-4 months in total): Schneider (CZ) and UT (NL)
APPLY HERE: PhD: AI for Electromagnetic Interference Filter Design (DC7) | Technische Universiteit Eindhoven
Host: IETR (CNRS, FR)
Main supervisor: Philippe Besnier
Co-supervisors/mentors: Dr. Ir. Anne Roc’h (TUe, NL), Dr. Ir. Charles Jullien (SAFRAN, FR), Prof. Jean-François Dupuy (CNRS/INSA Rennes, FR)
Duration: 36 months
Required profile: Electrical Engineering or Applied Mathematics
Desirable skills/interests: Electromagnetism, electromagnetic compatibility, statistics, machine learning, applied mathematics
Objectives: Recently, different techniques have been introduced to estimate the response of wiring systems according to their stochastic uncertainties using various approaches such as reliability techniques (risk analysis), polynomial chaos, decision-tree algorithms / random forests, neural network, kriging. None addresses uncertainty distribution This DC8 will work on the assessment of uncertainty distributions, which are key since governing the propagation. The work mainly aims at defining a practical and cost-efficient methodology to estimate the input distribution of uncertain parameters by design. This knowledge is a necessary condition to propagate them correctly for assessment of EMC output responses associated with cabling systems.
Secondments (2-4 months in total): SAFRAN (FR) and TUe (NL)
Host: TBU in Zlin (CZ)
Main supervisor: Dr. Stanislav Kovar (TBU, CZ)
Co-supervisors/mentors: Prof. Davy Pissoort (KUL, BE)/ Ing. Libor Valíček (Schneider, CZ)
Duration: 36 months
Required profile: Electrical Engineering/ Mechanical Engineering
Desirable skills/interests: Electromagnetic Compatibility, Computational electromagnetism, Metrology, Artificial Intelligence
Objectives: This project aims to enhance electromagnetic shield designs using evolutionary algorithms. DC will start by analyzing existing designs to set benchmarks. Next, DC will create a mathematical model to predict design effectiveness. Finally, AI will be implemented to optimize manufacturability and effectiveness, ensuring practical, high-performing solutions across industries.
Secondments (2-4 months in total): Schneider (CZ), IDIADA (CZ)
Host: KUL (BE)
Main supervisor: Prof. Davy Pissoort
Co-supervisors/mentors: Prof. Frank Leferink (UT, NL), ing. Ronny Deseine (Barco, BE), Prof. Mathias Verbeke (KUL, BE)
Duration: 36 months
Required profile: Electrical Engineering or Computer Science
Desirable skills/interests: Electromagnetism, artificial intelligence, machine learning, product design
Objectives: DC11 will investigate the potential of AI as a tool for design engineers to identify the origin and optimal solutions for EMC issues in the post-deployment phase of safety-critical products. Designing electronics with EMC considerations requires highly experienced engineers, whose expertise is built over many years of implementing, validating, and debugging design techniques. Despite this, pinpointing the origin of an EMC problem and finding the best solution can still be challenging. Additionally, integrating SSbD KPIs into solutions to control EMI has become an added challenge.
Secondments (2-4months in total): Barco (BE) and Philips (NL)
Host: KUL (BE)
Main supervisor: Prof. Mathias Verbeke (KUL, BE)
Co-supervisors/mentors: Prof. Philippe Besnier (IETR/CNRS, FR), ing. Ronny Deseine (Barco, BE), Prof. Tim Claeys (KUL, BE)
Duration: 36 months
Required profile: Electrical Engineering or Computer Science
Desirable skills/interests: Machine Learning, Artificial Intelligence, Electromagnetism
Objectives: DC13 aims to leverage AI for real-time anomaly detection in the electromagnetic environment, where anomalies are rare deviations from normal behavior. Due to the challenge of gathering a comprehensive dataset of anomalies, a semisupervised approach will be devised, using normal data to construct a model representing regular electromagnetic behavior. The project will combine “EMI footprints”, which capture normal variations through characteristic curves and statistical distributions, with traditional machine learning techniques (e.g., one-class support vector machines) and deep learning techniques (e.g., autoencoders/ transformers) to detect anomalies. By integrating these AI techniques with EMI footprints, DC13 seeks to develop an efficient methodology for real-time anomaly detection in the electromagnetic environment.
Secondments (2-4 months in total): Thales (NL) and IETR (CNRS, FR)
Host: University of Twente (UT, NL)
Main supervisor: Dr. Ir. Tom Hartman
Co-supervisors/mentors: Prof. Philippe Besnier, Ir. Hans Schipper, Prof. Raymond Veldhuis
Duration: 36 months
Required profile: Electrical Engineering
Desirable skills/interests: Electromagnetic Compatibility, Data Processing
Objectives: The unwanted emission or unwanted susceptibility of complex systems are always caused by aspects which are “forgotten” in the design. This is also the reason why even fantastic extreme difficult simulations fail to model accurately the EMI propagation in products. This DC will first ensure the data validity used for modeling EMI propagation at system level. S/he will consider “forgotten” parameters from Expert Systems made exceptionally available by industry, containing field diagnostic and accumulated precious (unpublished) knowledge from senior (EMC) experts.
Secondments (2-4 months in total): Thales (NL), KU Leuven (BE)
Host: De Montfort University
Main supervisor: Alistair Duffy
Co-supervisors/mentors: Hugo Gamboa, Hugo da Silva
Duration: 36 months
Required profile: Electrical Engineering
Desirable skills/interests: Electromagnetics, Electromagnetic Compatibility, AI, data comparison
Objectives: The feature selective validation (FSV) method compares data sets (particularly those for EMC applications) in a way that broadly matches the range of views of a group of experts. The hypothesis being tested in this project is whether it is possible to build this fundamental ‘algorithmic knowledge’ into ‘artificial intelligence’ to develop a more ‘algorithmic intelligence’ that might be better capable of interpreting data in a more natural and nuanced way, as well as requiring smaller training sets.
Secondments (2-4 months in total): PLUX, TUHH
Funded by the European Union under Grant Agreement No. 101169295 and UKRI (Public Body for Innovation and Research in UK, for position DC16). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Research Executive Agency (REA). Neither the European Union nor the granting authority can be held responsible for them.