PhD in Physical AI for Adaptive Robot Manipulation

PhD in Physical AI for Adaptive Robot Manipulation
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 March 2027 or later. You can submit your application via the link under 'how to apply'.

Title
PhD in Physical AI for Adaptive Robot Manipulation

Research area and project description
Applications are invited for a fully funded three-year PhD position at the Department of Electrical and Computer Engineering, Aarhus University. The position is part of STORM (Soft Tactile Memory for Safe Physical-AI Correction of Robotic Foundation Models), a new project funded by the Independent Research Fund Denmark (DFF). The candidate will join the Adaptive & Agentic AI (A3) Lab, headed by Dr. Behzad Bozorgtabar (main supervisor), and will work closely with Professor Ahmad Rafsanjani and the SDU Soft Robotics group, who are developing the soft tactile skin.

Research vision
How can a robot foundation model use physical feedback to correct an action during execution? Models such as vision-language-action (VLA) and world-action models let robots follow natural-language instructions, but they act on what they see rather than on what they feel. When a grasp begins to slip or a fragile object deforms, the camera view often looks fine until the damage is done. STORM gives robotic foundation models a sense of touch, so that a robot can notice when a manipulation is going wrong and correct itself safely while it acts.

PhD project
The candidate will develop the machine-learning core of STORM. The work spans three connected scientific questions, leaving room for the candidate to shape the specific algorithmic directions: (1) How can contact representations be learned from advanced tactile interfaces, including a soft-fluidic tactile skin developed at SDU? (2) How can predictive models forecast how contact states should evolve over short horizons under a policy's intended actions? (3) How can the mismatch between expected and felt contact drive parameter-efficient adaptation of pre-trained foundation policies while guaranteeing safety? Methods will be evaluated on public benchmark and real-world robotic setup, focusing on dexterous manipulation of fragile and unseen objects as well as safe human-robot interaction.

Environment
The project sits at the intersection of machine learning, Physical AI, robot foundation models (e.g., VLA and World-Action models), world models, tactile sensing, and adaptation algorithms—areas in which the A3 Lab publishes regularly at top-tier venues such as NeurIPS, ICLR, CVPR, and CoRL. The PhD includes a possible planned research stay at KTH Royal Institute of Technology in Stockholm, and close collaboration with the SDU team.

Qualifications and specific competences
This is a highly selective position; applicants are expected to meet the requirements below.

Education: A master’s degree (120 ECTS or equivalent) in Computer Science, Machine Learning, Artificial Intelligence, Robotics, Electrical/Computer Engineering, or a related quantitative field, completed or nearing completion before the start date.

Technical Skills: Advanced proficiency in Python and PyTorch, with hands-on experience training, fine-tuning, or adapting modern deep architecture such as Transformers, state-space/sequence models, and diffusion or flow-matching models.

Core Knowledge: A strong foundation in machine learning, deep learning, or computer vision, combined with a strong motivation to work on Physical AI, multimodal tactile sensing, world models, and online adaptation, etc.

Relevant Research Experience (one or more of the following is highly advantageous):

• Robot & Policy Learning: Imitation learning, behavior cloning, offline/model-based RL, or policy learning for manipulation (e.g., diffusion policies, Vision-Language-Action models, or World-Action models), as well as familiarity with manipulation benchmarks (e.g., LIBERO, CALVIN, or ManiSkill).
• World Models & Sequential Representation Learning: Self-supervised learning on temporal or multimodal sensor streams, action-conditioned video/dynamics prediction, latent world models, or uncertainty estimation.
• Model Adaptation & Post-Training: Test-time adaptation (TTA), continual/online learning, parameter-efficient fine-tuning (PEFT/LoRA), or safe/constrained policy updates.

Hands-on Robotics: Prior experience with physical robot platforms, grippers, tactile/force sensors, and ROS 2 is an advantage, but not strictly required for candidates with an exceptional machine learning background. However, a strong willingness to work with physical hardware and run real-robot experiments is required.

Track Record: Evidence of strong research potential—such as a strong master’s thesis, open-source research code, or publications/submissions at leading ML, vision, or robotics venues (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, CoRL, RSS, ICRA)—is a strong advantage.

Attributes: A mindset for scientific rigor, reproducibility, and open-source contribution, the ability to collaborate across algorithmic AI and physical hardware teams, and excellent written and spoken English.

Application Requirements (How to Apply)
Please ensure your application includes the following documents:

• Statement of Interest (1 page): Detailing your background in machine learning, computer vision or robotics, any work experience relevant to the position, and your motivation for joining the A3 Lab and the project.
• Curriculum Vitae: Including a publication list (if applicable) and a technical project page, with links to code repositories or videos of experiments where available.
• Academic Records: Transcripts and diplomas (Bachelor’s and Master’s).

Project Description Requirement: The candidate is required to use the project description from the PhD announcement.

Place of employment and place of work
The place of employment is Aarhus University, and the place of work is Adaptive & Agentic AI (A3) Lab, Department of Electrical and Computer Engineering (ECE), Faculty of Technical

Contacts
Applicants seeking further information regarding the PhD position are invited to contact:

• Behzad Bozorgtabar, e-mail: behzad@ece.au.dk (main supervisor)

For information about application requirements and mandatory attachments, please see our application guide. If answers cannot be found there, please contact:

• admission.gradschool.tech@au.dk
  

How to apply:
Please follow this link to submit your application.

Application deadline is 04 January 2027 at 23:59 CET

Preferred starting date is 01 March 2027

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

Please note:

• Only documents received prior to the application deadline will be evaluated. Thus, documents sent after deadline will not be considered.
• The programme committee may request further information or invite the applicant to attend an interview.
• Shortlisting will be used, which means that the evaluation committee only will evaluate the most relevant applications.

Formalities and salary range
Salary and terms as agreed between the Danish Ministry of Taxation and the Confederation of Professional Unions. You will be employed as an academic staff member at basic salary grades 4–8. As a PhD fellow you will also receive a monthly non-pensionable allowance.

Aarhus University’s ambition is to be an attractive and inspiring workplace for all and to foster a culture in which each individual has opportunities to thrive, achieve and develop. We view equality and diversity as assets, and we welcome all applicants.

The application must be submitted via the online recruitment system, which can be accessed under ‘How to apply’.