PhD Positions in Uncertainty Modelling and Optimisation Under Uncertainty

Catholic University of Leuven

Belgium

 
KU Leuven is a multi-campus organization, where collaboration across the campuses is an asset. The successful candidate takes the necessary initiatives to this end. The research group M-Group (Mechatronics Group) of KU Leuven focuses on the design, development and validation of dependable interconnected mechatronic systems, with dependability being defined as the ability of a system to provide its services in a way that can defensibly be trusted with modelling advanced uncertainty capability. More specifically, the Lab ‘Ultimate Factory’, a lab on dependability and control of interconnected systems, builds on connectivity and local intelligence in mechatronics systems towards applied research on the paradigm of Industry 4.0, “information and advanced (uncertainty) models as driver for change and value”. In the 'Ultimate Factory' every smart subsystem will collect data about themselves and aggregate them into useful information considering uncertainties. Precise and imprecise uncertainty will be identified, modelled, quantified, and included in the overall factory models. By means of a thorough connection between systems, this information can be shared across the entire smart factory. This flow of useful information is then used in the optimisation such as optimal control of the individual and collection of modules, optimal Machine Learning techniques, human centric optimal decision techniques, and safety aspects in order to increase the overall performance and reliability. The M-Group consist of several groups from the Departments of Mechanical Engineering, Electrical Engineering, and Computer Science, and operates in a truly interdisciplinary environment. Current research lines include dependability assurance of mission- and safety-critical systems, fault-tolerant and fail-operational hardware and software, software testing, distributed embedded sensors and sensor networks, marine and hydrogen energy systems, electromagnetic, and modelling advanced uncertainty compatibility.

Project

Although artificial intelligence (AI) has improved remarkably over the last years, its inability to deal with fundamental uncertainty severely limits its application. This project re-imagines AI with a proper treatment of the uncertainty stemming from our forcibly partial knowledge of the world. As currently practiced, AI cannot confidently make predictions robust enough to stand the test of data generated by processes different (even by tiny details, as shown by ‘adversarial’ results able to fool deep neural networks) from those studied at training time. While recognising this issue under different names (e.g. ‘overfitting’), traditional Machine Learning (ML) seems unable to address it in non-incremental ways. As a result, AI systems suffer from brittle behaviour, and find difficult to operate in new situations, e.g. adapting to driving in heavy rain or to other road users’ different styles of driving, e.g. deriving from cultural traits. This project reimagines AI through a proper treatment of the uncertainty stemming from our forcibly partial knowledge of the world. Epistemic AI’s paradoxical principle is that AI should first and foremost learn from the data it cannot see.

The research on optimisation under uncertainty will be supported by the EU H2020-FETOPEN-2018-2019-2020-01 project called Epistemic AI, an H2020-funded project due to start March 1st, 2021, coordinated by the Oxford Brooke University-UK, including KU Leuven and TU Delft as the second and third members in the project consortium. The main project goal: Epistemic AI’s overall objective is to create a new paradigm for a next-generation artificial intelligence providing worst-case guarantees on its predictions thanks to a proper modelling of real-world uncertainties.

The candidate will work on the granted 4-years EU FET-Open project (E-pi) as a part of his/her PhD and one of the objectives about “creating a novel mathematical framework for optimisation under epistemic uncertainty”, outputting sets of hypotheses (models), and leveraging techniques from theories of second-order uncertainty. He/she will work on modelling of uncertainty and optimisation under uncertainty, in both the foundations of uncertainty theory and frameworks for optimisation under uncertainty. He/she will also contribute to other work packages for the part on facilitating the translation of these new technologies into applications. We will also assist with exploitation and dissemination, together with the other partners at the E-pi consortium (from Oxford Brooks University – UK and TU Delft – The Netherlands).

KU Leuven has a full-time PhD positions to work in the M-Group: one under Computer Science Department (CS-PhD). The successful applicant will be appointed as PhD researchers in the CS-PhD. The vacant position is located at Bruges Campus and the research activities will be embedded in the co-located Mechatronics Group (M-Group). 

Profile

  • Candidate must hold a MSc degree on Computer Science, Applied Mathematics, Industrial Engineering, Mechatronics, or equivalent degree that gives access to KU Leuven Doctoral School PhD program.
  • Candidate must be fluent in English (knowing Dutch or having a certificate for a TOEFL/IELTS test is a plus).
  • We are seeking highly motivated, goal-oriented, individual, and pro-active candidates with a good background in (statistical) modelling, simulation, machine learning, optimisation, test (at the Ultimate Factory).
  • Experience in programming in Python, MATLAB, would be an advantage (PLC is a plus).
  • Candidate must be ambitious but above all team-players and communicative.
  • The candidate will have to comply with the KU Leuven regulation on doctoral degrees (https://admin.kuleuven.be/rd/doctoraatsreglement/en/phdregulation-set) 

Offer

The PhD position has a duration of 4 years.  The candidate starts with a one-year contract and will be extended to four years after a positive evaluation. 

We offer a fully funded PhD scholarship.  You will work in a brand new campus within a young and dynamic research group. 

We also offer health insurance and mobility support. 

Interested?

To apply you have to use the online application tool and provide the following information: 

1. A motivation letter

2. CV, including the names of two references

3. Transcripts of your bachelor and master studies

For more information please contact Keivan Shariatmadar, tel.: +32 50 66 48 68, email: keivan.shariatmadar@kuleuven.be or Prof. dr. ir. Hans Hallez, tel.: +32 50 66 48 38, email: hans.hallez@kuleuven.be.

You can apply for this job no later than January 18, 2021 via the
KU Leuven seeks to foster an environment where all talents can flourish, regardless of gender, age, cultural background, nationality or impairments. If you have any questions relating to accessibility or support, please contact us at diversiteit.HR@kuleuven.be.
  • Employment percentage: Voltijds
  • Location: Brugge
  • Apply before: January 18, 2021
  • Tags: Industriële Ingenieurswetenschappen


In your application, please refer to Professorpositions.com

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