PhD Position – Networks of complex neuron models: Learning in networks of spiking, highly non-linear neurons Full-time

PhD Position In Neuromorphic Computing And Analog Signal Processing

You will participate in an international team in an EU-funded Doctoral Network project called MINDnet. The project consists of 15 PhD students at 7 universities, one research center and two companies. The project has partners from eight different EU countries. All 15 PhD projects are within the overall theme of neuromorphic computing and analog signal processing, targeting applications in the fields of communication, sensing, geo-localization, space and biomedical. This PhD project will take place at PGI-14 Research Center Jülich. Apart from the time at PGI-14 there will be secondments of 3 months at Albira Tech SL (Barcelona, Spain), Technical University Graz (Austria), and University of Trento (Italy). There will also be regular meetings with the other 14 PhD students in the doctoral network, including 4 training schools and two workshops. As a participant of the project, you will become part of the group 'Computing in Dynamical Systems' at PGI-14.

Background MINDnet The exponential surge in artificial intelligence (AI), internet traffic, and online services demands a revolutionary leap in devices, computing architecture, and integration technologies. As digital computing struggles to keep up with the growing demand, in MINDnet will investigate neuromorphic computing as a promising solution to support such a demand by getting inspired by the brain's powerful and energy-efficient processing capabilities. MINDnet aims at addressing the challenge through a holistic optimization – from individual computing devices to the overall architecture, including a focus on applications, and training methods – across multiple technological platforms – photonics, electronics, biological neurons.

Responsibilities and tasks This PhD project aims to develop, verify, and benchmark learning rules in networks of complex spiking neuron models in the application field of geolocalization:

• Building up simulation model for networks of complex neurons with topologies close to neuroscience models
• Analyze dynamical states of spiking complex neuron networks with respect to network topology and neuron parameters
• Development of learning rules considering the strong non-linearities of the neurons
• Identify suitable application task in the field of geolocation and optimize network and learning rules accordingly
• Design, set up, and operate experimental systems for circuit-level measurements and data analysis

Your Profile:

• Masters degree in electrical/electronic engineering, computer engineering, computer science, physics, or related fields
• Coursework in algorithms, computational complexity theory, and information theory
• Relevant coursework and experience in spiking neural networks, and statistics
• A strong electronics background, including experience in the analysis and simulation of analog, digital, or mixed-signal circuits, including SPICE and related tools (LTspice, Cadence, MATLAB, Python)
• Excellent communication skills and ability to work in a team are essential
• Strong English skills will be required for the international working environment

Further obligations You are expected to travel to network partners on three assignments, each typically lasting 2–3 months. Additionally, you are expected to participate in outreach activities including, but not limited to, YouTube videos, social media updates, participation in public events and campaigns, as well as dissemination to popular press. Furthermore, due to the mobility rules of the Marie Skłodowska-Curie program, you must not have resided or carried out your main activity (work, studies, etc.) in Germany for more than 12 months in the 36 months immediately before your recruitment date.

Our Offer:

• ENVIRONMENT & INFRASTRUCTURE: You will participate in a world-leading, international research environment with state-of-the-art equipment
• RESEARCH & TEAM: Opportunity to work in the highly interdisciplinary and exciting field of neuromorphic and brain-inspired computing, collaborating with physicists, neuroscientists, materials scientists, and engineers
• WORK-LIFE BALANCE: We offer flexible working hours to help you balance your professional and personal life. You also have the option of flexible working (in terms of location), which is generally possible after consultation and in line with upcoming tasks and (on-site) appointments
• VACATION: 30 days of annual leave as well as an arrangement for bridging days (e.g. between Christmas and New Year's Day)
• KNOWLEDGE & DEVELOPMENT: Your professional development is important to us – we support you specifically and individually e.g., through training and networking opportunities specifically for doctoral candidates (JuDocS)
• SUPPORT FOR INTERNATIONAL EMPLOYEES: Our International Advisory Service makes it easier for international employees to get started
• FAIR REMUNERATION: Depending on your qualifications and assigned responsibilities, you will be classified according to pay group 13 (75%) of the TVöD-Bund. Additionally, you will receive a special payment (“Christmas bonus”) amounting to 60% of one month's salary. All information about the TVöD-Bund collective agreement can be found on the BMI website
• PERSPECTIVE: The position is initially for a fixed term of 3 years but with the prospect of longer-term employment

In addition to exciting tasks and a collegial working environment, we offer you much more. We welcome applications from people with diverse backgrounds, e.g. in terms of age, gender, disability, sexual orientation / identity, and social, ethnic and religious origin. A diverse and inclusive working environment with equal opportunities in which everyone can realize their potential is important to us. The following links provide further information on diversity and equal opportunities and on the targeted promotion of women.

Place of employment: Aachen Start for the grant : January 1, 2026. Preferred start of employment: between May and September 2026 This project has received funding from the European Union's Horizon 2020 Research and innovation Programme under the Marie Skłodowska-Curie Grant Agreement No. 101226674