PhD Position in Physical AI: Adaptive Foundation Models for Robotics

Aarhus University; Denmark; A distancia; DKK 260.000 - 340.000; Jornada completa

Descripción de la vacante

The PhD Position in Physical AI at Aarhus University invites applications for a three-year fellowship in the Electrical and Computer Engineering program, starting 01 January 2027 or later.

The successful candidate will join the A3 Lab and work on adaptive foundation models for robotics, focusing on learning, perception, planning and edge intelligence, with publications in top ML/vision venues. Strong ML background and evidence of research potential are required.

Formación

• Master's degree in CS/EE/Robotics with strong ML foundations.
• Proven research potential via a substantial thesis, project, code contribution or publication.
• Demonstrated ability to implement, train and evaluate deep‑learning models.

Responsabilidades

• Conduct original research in Physical AI for robotics.
• Develop learning algorithms and evaluation on public datasets and simulations.
• Publish results in top machine learning and robotics venues.

Conocimientos

Python

PyTorch

Deep learning

Machine learning

Educación

Master's degree in computer science, electrical or computer engineering, robotics, machine learning or a related field

Herramientas

PyTorch

Descripción del empleo

Applicants are invited for a PhD fellowship/scholarship at Graduate School of Technical Sciences, Aarhus University, Denmark, within the Electrical and Computer Engineering programme. The position is available from 01 January 2027 or later.

Title

PhD Position in Physical AI: Adaptive Foundation Models for Robotics

Research area and project description

Applications are invited for a three-year PhD position in Physical AI at Aarhus University's Department of Electrical and Computer Engineering. The candidate will join the Adaptive & Agentic AI (A3) Lab, supervised by Associate Professor Behzad Bozorgtabar and co-supervised by Professor Qi Zhang.

Research vision

How can intelligent robots understand instructions, anticipate the consequences of actions, and adapt reliably when the physical world changes? The project connects multimodal perception, reasoning and action with predictive learning and edge intelligence.

Research directions and objectives

Vision-language-action models (VLAs)

Investigate VLA models and multimodal representations that connect visual observations and language instructions to robot behaviour, including learning from demonstrations and generalisation to unfamiliar tasks, objects or environments.

World models and planning

Develop models that predict how the physical world responds to actions, supporting planning and learning from interaction. Possible directions include learning from video, demonstrations and simulation, and transferring knowledge across robot configurations.

Adaptation and edge intelligence

Develop efficient methods for maintaining reliable behaviour under changing environments, sensing conditions and resource constraints. Topics may include test-time and continual adaptation, uncertainty-aware decision-making, and efficient inference and model updates under latency, memory and energy limits.

The precise research focus will be developed with the successful candidate within these connected directions. Research will emphasise new learning algorithms and rigorous evaluation using public datasets, simulation and, where available, robotic and edge-computing platforms. Evaluation will consider task success, generalisation, reliability and computational efficiency. The goal is original research for leading machine-learning, computer-vision and robotics venues. The successful candidate will be expected to conduct original, high-quality research targeting publications at top-tier machine learning, computer vision and robotics conferences, including NeurIPS, ICML, ICLR, CVPR, ICCV, CoRL, RSS and ICRA.

Project description

For technical reasons, you must upload a project description. Please simply copy the project description above and upload it as a PDF in the application.

Qualifications and specific competences:

Essential qualifications

A master’s degree (120 ECTS or equivalent), completed by enrolment, in computer science, electrical or computer engineering, robotics, machine learning or a related field; strong academic results and foundations in machine learning, linear algebra, probability and optimisation; and strong Python and PyTorch (or comparable framework) skills.

Only applicants who demonstrate substantial hands‑on experience implementing, training and evaluating deep‑learning models will be considered. General interest in AI or experience limited to running tutorials or using pretrained‑model APIs is not sufficient.

Applicants must provide evidence of research potential through a substantial thesis, research project, code contribution or publication, clearly identifying their own technical contribution.

Application deadline

01 November 2026 at 23:59 CET.

Preferred starting date is 01 January 2027.

Competitive