PhD position in Neuromorphic Devices and Circuits for next-generation sensing and computing

Organisation/Company

Uppsala universitet

Department

Uppsala University, Department of Electrical Engineering

Research Field

Computer science
Engineering
Information science
Physics
Technology

Researcher Profile

First Stage Researcher (R1)

Country

Sweden

Application Deadline

25 Feb 2026 - 22:59 (UTC)

Type of Contract

Temporary

Job Status

Full-time

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

Electrification and digitalisation are among the major areas for the future transition to sustainable societies. The Department of Electrical Engineering conducts successful research and education in these areas - including renewable energy sources, electric vehicles, industrial IoT, 6G communication, and wireless sensor networks, as well as research and education in Life Science, health technology, smart electronic sensors, and medical systems. The Department of Electrical Engineering is an international environment with around 160 employees who contribute to important technological energy and health at the Ångström Laboratory.

We are now looking for a PhD student in Neuromorphic Devices and Circuits for next-generation sensing and computing.

Come and join us!
The position is placed at the Division of Solid-State Electronics, within the Department of Electrical Engineering. Here you will find a welcoming working environment with an active doctoral student network and a variety of experimental projects. The division collaborates with Swedish companies – both public and private – as well as other stakeholders within the various research areas. We look forward to receiving your application. Join us in building the future!

Read more about our benefits and what it is like to work at Uppsala University.

Project description

Human brains can process complex, dynamic, and real-time information with remarkable efficiency after millions of years of evolution. Neuromorphic computing, inspired by how the human brain functions, offers a promising solution to dramatically lowering power consumption and enables fast and adaptive information processing in today’s Zettabyte-scale data era. Artificial neurons and synapses are the core building blocks of neuromorphic systems. Yet currently, these neuromorphic circuits still fall far short of the efficiency and functionality of their biological counterparts.

This project aims to overcome these limitations through a synergistic circuit–device co-design that jointly optimizes both circuit architectures and component properties, ensuring that intrinsic device physics effectively translate into significant system-level improvements. In this project, we will develop neuromorphic devices with inherent biological resemblance and compact, low-power neuromorphic circuits. We will also simulate high-efficiency spiking neural networks (SNN) and build neuromorphic sensory systems to validate performance and explore broad biomedical and other potential applications. The outcomes will contribute fundamental knowledge and new technologies that move us closer to sensing and computing systems approaching the efficiency of the human brain.

This project involves both experimental development and theoretical modelling. Therefore, it requires the candidate to have a solid background in physics, electronics, and mathematics, along with strong practical experimental skills.

We have an exciting research environment shaped jointly by the doctoral student and the research team. The doctoral student will be supervised by at least two supervisors and will carry out the project within a multidisciplinary setting where scientists of complementary backgrounds collaborate.

Duties

• Design and fabricate neuromorphic devices with intrinsic neuromimetic dynamics.
• Design and fabricate compact, low-power neuromorphic circuits using the developed devices.
• Simulate high-efficiency spiking neural networks (SNNs) based on the developed circuits to evaluate system-level performance improvements.
• Develop neuromorphic sensory systems for biomedical and other application domains.
• Model and simulate neuromorphic devices, circuits and systems.
• Investigate spike-based signal processing and neuronal dynamics at different scales and explore a broad range of potential applications.
• The main duties of a doctoral student are confined to their research studies, which include participating in the research project and taking third-cycle courses for doctoral students.
• Teaching and other departmental duties may be part of the position, to a maximum of 20% of the study time. 

Qualifications required

To meet the entry requirements for doctoral studies, you must

• hold a Master’s (second-cycle) degree in electronics (electronic science/engineering), electrical engineering, (applied/engineering) physics, or in a similar field, or
• have completed at least 240 credits in higher education with at least 60 credits at Master’s level including an independent project worth at least 15 credits, or
• have acquired substantially equivalent knowledge in some other way.

We are looking for candidates with:

• interest in developing neuromorphic devices, circuits and systems, including both hardware implementations and computational models,
• very good study results.

We place great emphasis on personal qualities such as creativity, perseverance, the ability to work independently, and a structured approach to problem-solving. You should have strong motivation, a critical mindset, strong problem-solving skills, good collaboration skills, and the ability to communicate complex ideas clearly. Being able to organise and structure your work efficiently is also key to succeeding in the role.

The candidate must have good communication skills in English, both orally and in writing.

Qualifications desired

Experience in electronic (especially neuromorphic) device, circuit and system design, clean-room fabrication, ferroelectric (or antiferroelectric) materials and devices, memristors, tactile sensors, FPGA/MCU/API development, machine learning, spiking neural network, optimisation, programming, or dynamical systems.

About the employment

Fixed term position 48 months, in accordance with Chapter 5, Section 7 of the Higher Education Ordinance of the Swedish Laws. Full-time. Start date 2026-06-01 or as agreed. Location: Uppsala.

Rules governing doctoral students are set out in the Higher Education Ordinance Chapter 5, Sections 1-7 and in Uppsala University's rules and guidelines.

For further information about the position, please contact
Assistant Professor, Libo Chen, libo.chen@angstrom.uu.se , +46 738855263.                                 Professor, Shi-Li Zhang, shili.zhang@angstrom.uu.se , +46 184717247.                                          Associate Professor, Ted Johansson, ted.johansson@angstrom.uu.se , +46 184713017.

Application instructions
The application should include:

• 1) a cover letter, briefly describing yourself, your education, your previous experiences, your research interests, your envisioned contribution to the project, and why you wish to pursue doctoral studies; the cover letter should include the heading Suitability for this position, containing a self-assessment explaining why you would be the right candidate for this position;
• 2) CV;
• 3) copies of your degrees and course transcripts with grades (translated to English or Swedish), as well as a copy of your Master’s thesis (or a draft thereof);
• 4) any publications;
• 5) the names and contact details of two referees (email address and telephone number); and
• 6) any other documents you wish to refer to.

The application should be written in English.

You are welcome to submit your application no later thanFebruary 25, 2026. UFV-PA 2025/3821.

Website

https://uu.varbi.com/en/what:job/jobID:883006/type:job/where:39/apply:1

Research Field

Computer science

Education Level

Master Degree or equivalent

Research Field

Engineering

Education Level

Master Degree or equivalent

Research Field

Information science

Education Level

Master Degree or equivalent

Research Field

Physics

Education Level

Master Degree or equivalent

Research Field

Technology

Education Level

Master Degree or equivalent

Research Field

Computer science

Years of Research Experience

1 - 4

Research Field

Engineering

Years of Research Experience

1 - 4

Research Field

Information science

Years of Research Experience

1 - 4

Research Field

Physics

Years of Research Experience

1 - 4

Research Field

Technology

Years of Research Experience

1 - 4

Number of offers available

1

Company/Institute

Uppsala universitet

Country

Sweden

City

Uppsala

Geofield

City

Uppsala

Website

http://www.uu.se/en/about-uu/join-us/jobs/

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