PhD Position in Systems and Control Theory for Energy-based Learning

12 Sep 2026
Job Information

Organisation/Company

University of Groningen

Research Field

Engineering » Control engineering
Engineering » Electrical engineering

Researcher Profile

First Stage Researcher (R1)

Application Deadline

10 Nov 2026 - 22:00 (UTC)

Country

Netherlands

Type of Contract

Temporary

Job Status

Not Applicable

Hours Per Week

38.0

Is the job funded through the EU Research Framework Programme?

Not funded by a EU programme

Is the Job related to staff position within a Research Infrastructure?

No

Offer Description

Are you excited about developing new mathematical foundations for energy-efficient computing? Do you want to contribute to cutting-edge research at the intersection of systems and control theory, optimization, circuit theory, and neuromorphic computing?

The University of Groningen is seeking a highly motivated PhD candidate to work on a fundamental research project on systems and control theory for learning in neuromorphic circuits. Neuromorphic computing is an analog, brain-inspired computing paradigm with the potential to drastically reduce energy consumption while enabling faster inference than conventional digital architectures. A major challenge, however, is the development and analysis of dedicated algorithms for training analog circuits directly from data.

In this PhD project, you will develop a novel system-theoretic framework for learning in analog circuits and dissipative networks. We will view learning as a feedback interconnection of continuous-time (circuit) dynamics and an optimization algorithm. The key idea is to develop algorithms that minimise cost functions inspired by notions of energy, leading to highly efficient, local learning rules.

What are you going to do?

As a PhD candidate, you will develop mathematical theory for learning in nonlinear and dynamic circuits. Building on preliminary results for resistive circuits, you will study circuits containing memristive and capacitive elements, as well as more general dissipative networks. The project combines systems and control theory, circuit theory, optimization, and machine learning, with the ultimate goal of advancing the mathematical foundations of physics-based learning.

Your responsibilities include:

• Developing a system-theoretic framework that models learning as the feedback interconnection between continuous-time circuit dynamics and optimization algorithms.
• Designing novel energy-based learning algorithms for training analog circuits directly from input-output data.
• Developing fully decentralised learning rules that rely on local circuit information and are suitable for large-scale systems.
• Establishing rigorous theoretical guarantees for convergence and scalability of the proposed learning algorithms.
• Extending the theory from analog circuits to more general dissipative networks.
• Testing and validating the developed methods.
• Publishing research findings in leading international journals and conferences and presenting your work at scientific meetings.
• Contributing to teaching activities and supervising Bachelor's and Master's students where appropriate.

Where to apply

Website

https://www.academictransfer.com/en/jobs/363887/phd-position-in-systems-and-con…

Requirements
Specific Requirements

We are looking for an enthusiastic researcher who enjoys solving challenging problems and working in an international research environment.

You should have:

• A Master's degree in Systems and Control, (Applied) Mathematics, Electrical Engineering, or a closely related field.
• A strong mathematical background and an interest in conducting theoretical research.
• Excellent English communication skills, both written and spoken.
• Strong analytical abilities, creativity, persistence, and the ability to collaborate effectively.
• Familiarity with circuit theory, networked systems, machine learning, or neuromorphic computing is considered an advantage, but is not required.

Additional Information
Benefits

What can you expect from us?

• 232 vacation hours per year, based on a 38-hour workweek (1.0 FTE). You can also work more or fewer hours in exchange for more or fewer free hours. For example, with a 40-hour workweek, you save 96 extra free hours, and with a 36-hour workweek, you lose 96 hours.
• End-of-year bonus of 8.3% and 8% holiday allowance.
• Extensive opportunities for personal and professional development.

Selection process

Interested?

Does this vacancy appeal to you? If so, click on the button below and apply straightaway. Please add the following documents to your application:

● Motivation letter (1-2 pages)

● Curriculum Vitae

● Academic transcripts (BSc and MSc)

● Contact details of two academic references

● Copy of MSc thesis (or a representative publication, if available)

Information about applying

When scheduling meetings, we will take your schedule into account as much as possible. The University of Groningen considers social safety important. We strive to be a university where staff and students feel respected and at home, regardless of differences in background, experiences, perspectives, and identity. For more information, see also our page about our diversity policy .

Our selection procedure follows the guidelines of the NVP application code

Acquisition is not appreciated.

Additional comments

Do you have any questions or need more information?

Questions about the content of the job?
Henk van Waarde (Assistant Professor): [email protected]

Questions about your application process?
Henk van Waarde (Assistant Professor): [email protected]

Website for additional job details

https://www.academictransfer.com/363887/

Work Location(s)

Number of offers available

1

Company/Institute

University of Groningen

Country

Netherlands

City

Groningen

Postal Code

9712 CP

Street

Broerstraat 5

Contact

City

Groningen

Website

http://www.rug.nl/

Street

Broerstraat 5

Postal Code

9712 CP

STATUS: EXPIRED

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