Doctoral student in Computer Science

Description of the workplace

The division for Robotics and Semantic Systems (RSS) consists of several, tightly collaborating research groups conducting research and education in Robotics and AI, Intelligent Autonomous Systems, Robot Learning, Machine Learning, Human-Robot Interaction and Natural Language Processing. 

The division runs, together with the Department for Automatic Control, the RobotLab LTH, a research and education facility equipped with a multitude of robotic and related systems, ranging from small drones and UAVs to classic industrial manipulators. The research environment conducts internationally recognized research in robot learning, embodied AI, robot manipulation, semantic scene understanding, and cognitive robotics. The group combines machine learning, computer vision, robot control, and AI planning for real-world robotic systems, with a strong emphasis on robust and trustworthy autonomy.

The doctoral student will work in close collaboration with researchers within the ELLIIT framework and interact with interdisciplinary teams in robotics, machine learning, perception, and AI reasoning. The workplace provides access to advanced robot platforms including a dual arm KUKA iiwa and UR5e manipulators, ROS 2-based robotic infrastructures, simulation environments, and modern GPU computing resources.

The research environment values openness, collaboration, scientific rigor, and a supportive working atmosphere. The group actively promotes diversity, inclusion, and a positive work environment.

Being a doctoral student 

As a doctoral student, you are both admitted as a student and employed at Lund University. 

As a doctoral student, you will be trained in a scientific approach. In short, you will be trained to think critically and analytically, to solve problems independently using the right methods, and to develop an awareness of research ethics. In addition, you will have the opportunity to work on projects, to develop your leadership and pedagogical skills. Throughout your studies, you will be guided by supervisors. Doctoral studies end with a thesis and a doctoral degree.

More about being a doctoral student at LTH on lth.se.

Subject and project description

The doctoral position is within Computer Science with focus on embodied AI, robot learning, and trustworthy Vision-Language-Action (VLA) systems. The project is part of the ELLIIT research initiative “A Robust and Reliable Vision-Language-Action Interface”.

Recent advances in Large Language Models (LLMs), Large Reasoning Models (LRMs), and Vision-Language-Action (VLA) systems have enabled robots to perform increasingly complex tasks from multimodal sensory input and natural language instructions. However, current systems still lack robust situational awareness, introspection, uncertainty estimation, and reliable long-horizon planning capabilities  .

The doctoral project focuses on developing trustworthy robotic systems that combine:

• symbolic and semantic world models
• large reasoning models for long-horizon planning
• vision-language-action models for robot execution
• out-of-distribution (OOD) detection and uncertainty estimation
• adaptive robot behavior based on predicted task success probabilities

A central research challenge is to develop robot systems capable of reasoning about whether a planned action is likely to succeed in a given situation and adapt execution parameters accordingly, for example by reducing speed or selecting alternative actions in uncertain situations.

The project will investigate how symbolic world models, semantic scene representations, and uncertainty-aware reasoning can be integrated into embodied AI systems for improved robustness, explainability, and safety in real-world robotic applications.

Work duties

You will primarily devote yourself to your doctoral programme, which includes participation in research projects as well as third cycle courses, seminars and conferences. 

The work duties include:

• conducting research in embodied AI, robot learning, and uncertainty-aware robotic reasoning.
• developing symbolic and semantic world models for robotic planning.
• integrating Large Reasoning Models (LRMs) with Vision-Language-Action (VLA) systems.
• developing methods for out-of-distribution detection and uncertainty estimation in robotic decision-making.
• implementing and evaluating robotic systems on real robot platforms.
• publishing research results in leading international conferences and journals.
• collaborating with researchers and industrial partners within robotics and AI.
• contributing to maintenance and development of robotic research infrastructure.

The duties also include participation in teaching and other departmental work (however, a maximum of 20% of working hours).

Qualifications 

To be eligible for admission and employment as a doctoral student, you must fulfil the requirements below.

Admission requirements 

A person meets the general admission requirements for third-cycle courses and study programmes if the applicant: 

• has been awarded a second-cycle qualification, or 
• has satisfied the requirements for courses comprising at least 240 credits of which at least 60 credits were awarded in the second cycle, or 
• has acquired substantially equivalent knowledge in some other way in Sweden or abroad.

A person meets the specific admission requirements for third cycle studies in Computer Science if the applicant has: 

• at least 60 second-cycle credits at an advanced level with relevance for the research topic, or
• an MSc in Engineering in Computer Science and Engineering, Electrical Engineering, Information and Communication Technology, Engineering Physics or Engineering Mathematics 

Additional requirements

In order to complete the doctoral programme in question, the following are also required: 

• strong knowledge in machine learning, robotics, computer vision.
• Very good experience with modeling and learning from data.
• experience with robot programming and modern robotic software frameworks such as
  ROS 2.
• programming experience in Python and/or C++.
• experience with deep learning frameworks such as PyTorch.
• good ability to work independently and to formulate and tackle research problems. 
• good written and oral communication skills.
• good ability to cooperate.
• very good knowledge of English, spoken and written 

Other qualifications (advantages)

For the doctoral programme in question, the following are considered as other qualifications: 

• experience with diffusion policies.
• experience with robot manipulation and real-world robotic systems.
• experience with simulation environments such as Isaac Lab or MuJoCo.
• previous scientific publications in robotics, machine learning, or AI.
• experience with KUKA iiwa, UR5e, or related robotic platforms.
• experience with large language models or transformer-based architectures.

We offer

Lund University is a public authority which means that employees get particular benefits, generous annual leave and an advantageous occupational pension scheme.

More about working at Lund University on lu.se.

About the employment 

The employment is a fixed-term employment at full time, starting autumn 2026.  Third cycle studies at LTH consist of full-time studies for 4 years. In the case of teaching and other departmental duties, the employment is extended accordingly. Doctoral studentships are regulated in the Higher Education Ordinance (1993:100), chapter 5, 1-7 §§.

More about terms of employment for doctoral students on Lund University’s Staffpages. 

How to apply

Applications shall be written in English and include: 

• CV and a cover letter stating the reasons why you are interested in the doctoral programme/employment and in what way the research project corresponds to your interests and educational background. 
• Copies of issued study certificates and/or awarded degree certificates. These must confirm that you meet the general and specific admission requirements for the doctoral programme and show that you have the subject knowledge required for the doctoral programme project. 
• Other documents you wish to be considered (grade transcripts, contact information for your references, letters of recommendation, etc.)

We welcome your application.