Postdoc position on Federated/Continual Learning for Time-Series IoT Data (TRUMAN Project)

20 Dec 2025
Job Information

Organisation/Company

University of Twente (UT)

Research Field

Computer science » Informatics
Computer science » Programming
Engineering » Computer engineering
Engineering » Electrical engineering

Researcher Profile

Recognised Researcher (R2)

Country

Netherlands

Application Deadline

15 Feb 2026 - 22:59 (UTC)

Type of Contract

Temporary

Job Status

Not Applicable

Hours Per Week

40.0

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

In this role, you will address the intricate challenge of enabling AI to learn continuously and collaboratively from wearable or mobile sensor data without compromising user privacy. Your efforts and collaborations with other European Union partners will contribute to the advancement of privacy-preserving artificial intelligence for the benefit of humanity.
What You Will Do:

• Research (Federated Continual Learning): You will develop novel and privacy-preserving algorithms that allow distributed devices (smartphones, wearables) to learn from new data streams over time (Continual Learning) while collaborating globally (Federated Learning).
• Analyze Mobile & Wearable Data: You will work with noisy, high-frequency time-series sensor data (e.g., accelerometers, gyroscopes, biosignals) from mobile IoT devices. You will address specific challenges such as user heterogeneity, concept drift in human behavior, and the need for personalization.
• Resource-Efficient AI: You will ensure your models are lightweight enough to run on edge devices, balancing accuracy with energy consumption and computational constraints.
• Project Coordination: You will assist the PI in managing work package tasks. This includes tracking technical progress, coordinating with partners on use-case requirements, and contributing to project deliverables.

About the TRUMAN Project:
The goal of TRUMAN is to design and develop generic technologies and methodologies for improving AI systems’ resilience against security, privacy, and fairness attacks, as well as to increase the trust that their users have in these systems, while accounting for different phases of the AI life cycle, starting from data collection through training and deployment. Your specific focus (Work Package 3) is on Human-In-The-Loop AI and Continual Learning. We aim to build systems that excel in performance but prioritize robustness, fairness, and privacy, critical requirements when dealing with time-series data. More info about the TRUMAN project can be found here .

Where to apply

Website

https://www.academictransfer.com/en/jobs/357341/postdoc-position-on-federatedco…

Requirements
Specific Requirements

We are looking for a researcher who sits at the intersection of Pervasive/Mobile Computing and Machine Learning.

• Background: You hold a PhD in Computer Science, Machine Learning, Electrical Engineering, Embedded Systems or related fields.
• Core Expertise:
• Strong expertise in Federated Learning and/or Continual Learning, with a solid understanding of the challenges involved in combining the two paradigms (FCL).
• Demonstrated ability to translate theoretical methods into working prototypes or deployable systems.
• Data and System
• Proven track record analyzing time-series IoT sensor data from wearables or mobile devices (e.g., human activity recognition, health and wellness monitoring, behavioral sensing).
• Comfortable working with imperfect, noisy, heterogeneous sensor data, and applying robust preprocessing, feature extraction, and validation workflows.
• Solid understanding of the practical constraints of mobile and embedded hardware (battery limitations, intermittent connectivity, limited compute/storage).
• Experience implementing or optimizing models for on-device or edge execution is a strong plus.
• Project Organizational Skills: You are well-organized and capable of coordinating a research work package, including planning, documentation, reporting, and partner communication within a collaborative project.
• Communication:
• Excellent communication skills in English.
• Comfortable working in interdisciplinary and international teams, engaging with both academic and industrial partners.

Additional Information
Benefits

• A full-time position for 1.5 or 2 years.
• The salary and associated conditions are in accordance with the collective labour agreement for Dutch universities (CAO-NU).
• You will receive a gross monthly salary ranging from € 3.546,- to € 5.538,- depending on experience and qualifications.
• There are excellent benefits, including a holiday allowance of 8% of the gross annual salary, an end-of-year bonus of 8.3%, and a solid pension scheme.
• A minimum of 232 leave hours in case of full-time employment based on a formal workweek of 38 hours. A full-time employment in practice means 40 hours a week, therefore resulting in 96 extra leave hours on an annual basis.
• Free access to sports facilities on campus.
• A family-friendly institution that offers parental leave (both paid and unpaid).
• Excellent career support and courses for your professional and personal development.
• The opportunity to pioneer privacy-preserving AI while gaining experience in European project management.

Additional comments

Are you interested in this position? Please send your application via the 'Apply now' button below beforeFebruary 16, 2026, and include:

• CV (including publication list)
• A cover letter highlighting your experience with time-series sensor data and distributed and or continual learning paradigms
• Research Statement.
• Two reference letters (one from your PhD supervisor).

Please contact Dr. Özlem Durmaz Incel if you have any additional questions via the following email adress: ozlem.durmaz@utwente.nl .

Website for additional job details

https://www.academictransfer.com/357341/

Work Location(s)

Number of offers available

1

Company/Institute

Universiteit Twente

Country

Netherlands

City

Enschede

Postal Code

7522NB

Street

Drienerlolaan 5

Geofield

Contact

City

Enschede

Website

http://www.universiteittwente.nl/

Street

Drienerlolaan 5

Postal Code

7522 NB

STATUS: EXPIRED

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