Postdoctoral research position in Physics-Informed Machine Learning for Explainable and Generalizable Robot Control

7 Jan 2026
Job Information

Organisation/Company

Czech Technical University in Prague

Department

Faculty of Electrical Engineering

Research Field

Computer science » Cybernetics
Computer science » Informatics

Researcher Profile

Recognised Researcher (R2)
Established Researcher (R3)

Positions

Postdoc Positions

Country

Czech Republic

Application Deadline

15 Feb 2026 - 23:59 (Europe/Prague)

Type of Contract

Temporary

Job Status

Full-time

Hours Per Week

40

Offer Starting Date

1 Mar 2026

Is the job funded through the EU Research Framework Programme?

Not funded by a EU programme

Reference Number

#3-31

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

No

Offer Description

Czech Technical University in Prague (CTU) offers a fellowship program, the CTU Global Postdoc Fellowship. This new and attractive two-year fellowship-program offers excellent researchers who have recently completed their PhD the chance to continue their research career at CTU. Fellows receive a two year fellowship and become members of a team led by a mentor.

The scholarship applicant must meet the following conditions on the date of application:

• be no more than 7 years since obtaining the first Ph.D. degree,
• Ph.D. studies at a university outside the Czech Republic or have completed at least a one-year working research stay abroad (outside the Czech Republic),
• be an author (co-author) of three or more publications in a journal with IF or CORE A*/A conference paper.

Robust machine control assumes modeling of robot-environment interactions. An example may include an outdoor autonomous ground robot that needs to be aware of its model and how the terrain will interact with it when a control sequence is executed. A flying robot may benefit from knowing the wind field ahead to model aerodynamic forces correctly.

However, building robust perception systems that can efficiently adapt in a self-supervised manner to novel environments remains a significant challenge. We identify three core issues: (i) black-box models that ignore the robot's physical embodiment suffer from poor generalization, weak explainability, and limited transferability; (ii) sample-inefficient learning requires large volumes of annotated, domain-specific data; and (iii) complex architectures with tightly coupled components hinder modular adaptation. To address these limitations, we research a physics-guided machine learning framework that integrates physical knowledge with data-driven methods. Physical knowledge includes, among others, kinematics and the dynamics of the robot, terrain interaction and contact models, and environmental physics, such as wind. The physics can be incorporated in various ways. Two methods now researched most intensively are i) trainable machine learning pipelines may embed differentiable physical models, and ii) the learning process may be informed by constraining the predicted variable to obey physical laws; we can see it as physics-informed losses. We will seek new ways to embed the physics.

Our approach aims to enable explainable, embodiment-aware, and probabilistically consistent adaptation from onboard sensory data via end-to-end differentiable architectures, enhancing robustness, efficiency, and generalization across diverse robotic platforms and environments.

Group and supervision: Research will be conducted within the Vision and  Robotics Group. The group has extensive experience with real robotics, such as successful participation in the DARPA SubT challenge (https://robotics.fel.cvut.cz/cras/darpa-subt/ ) and several state-of-the-art robotics platforms and sensors (https://robotics.fel.cvut.cz/cras/robots/ ). The Department has access to a high-performance computational cluster dedicated to artificial intelligence research and developments using traditional multi-CPU systems, but also GPUs.

Where to apply

E-mail

drimakat@fel.cvut.cz

Requirements

Research Field

Engineering

Education Level

PhD or equivalent

Skills/Qualifications

We seek highly motivated applicants with a PhD in robotics, AI, or related fields and a proven track record relevant to the topic - publications in top journals or conferences (e.g. computer vision (CVPR/ICCV/ECCV), machine learning (NeurIPS/ICML), or robotics (ICRA, IROS, RSS, CoRL; IEEE-TRO, IJRR). 

Languages

ENGLISH

Level

Excellent

Internal Application form(s) needed
20231016-application-form-electronic-signature_7.pdf
English
(41.96 KB - PDF)
Download
20231016-application-form-hand-signature_7.pdf
English
(41.61 KB - PDF)
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Additional Information
Benefits

Salary: 75 000 CZK

Selection process

Applications will be assessed by a committee, based on the documents submitted by applicants. The mentor has a strong vote in the selection process.

To apply for the CTU Global Postdoc Fellowship you need the following documents in English:

• CV, including a list of publications (max. 4 pages). At least three Impact Factor journal publications are expected or CORE A*/A conference paper. Papers accepted for publication yet waiting to be printed, do count if a proof of acceptance is provided.
• Motivation letter (max. 2 pages).
• PhD certificate (copy).
• A cover letter (See Application for CTU Global Postdoc Fellowship form)
• You may attach other documents supporting your application, such as recommendation letters etc.

The job start date is negotiable. 

Additional comments

Mentor: Karel Zimmermann , Faculty of Electrical Engineering, Department of Cybernetics, zimmerk@fel.cvut.cz

https://cmp.felk.cvut.cz/~zimmerk 

https://scholar.google.cz/citations?hl=en&user=UROU6RgAAAAJ&view_op=list_works&sortby=pubdate  

https://sites.google.com/view/karelzimermann/publications  

H-index (WOS) 14

ORCID https://orcid.org/0000-0002-8898-4512  

Website for additional job details

https://www.cvut.cz/en/ctu-global-postdoc-fellowship
https://cyber.felk.cvut.cz/vras

Work Location(s)

Number of offers available

1

Company/Institute

Czech Technical University in Prague / Faculty of Electrical Engineering

Country

Czech Republic

City

Prague

Postal Code

16627

Street

Technická 2

Geofield

Contact

State/Province

Czech Republic

City

Prague

Website

https://www.cvut.cz/en

Street

Jugoslávských partyzánů 1580/3

Postal Code

16000

E-Mail

drimakat@fel.cvut.cz

STATUS: EXPIRED

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