Lund University, LTH, Department of Automatic Control

Lund University was founded in 1666 and is repeatedly ranked among the world’s top universities. The University has around 47 000 students and more than 8 800 staff based in Lund, Helsingborg and Malmö. We are united in our efforts to understand, explain and improve our world and the human condition.

Lund University welcomes applicants with diverse backgrounds and experiences. We regard gender equality and diversity as a strength and an asset.

About the Department

The Department of Automatic Control at Lund University, Faculty of Engineering (LTH) invites applications for a position as Senior Lecturer.

Optimization, machine learning, and control theory together form a central toolbox for understanding, analyzing, and controlling complex systems. These fields span deep mathematical theory and algorithm development as well as engineering methods that enable robust and efficient practical solutions. As society and technology evolve toward increasingly large-scale, data-intensive, and interconnected systems, the need for scalable methods and reliable guarantees becomes increasingly clear — both in research and in education.

At the Department of Automatic Control, LTH, research and teaching are conducted in an international environment with around 60 employees, including doctoral students, postdocs, and faculty with broad subject expertise. The department has a strong tradition in systems and control theory, while also expanding toward large-scale optimization, data-driven methods, and machine learning.

The work environment is characterized by an open and ambitious atmosphere, with collaborations both within academia and with industrial partners, nationally and internationally.

Lund University and the Department of Automatic Control welcome applicants with diverse backgrounds and experiences. We view gender equality and diversity as strengths and assets.

The department currently has three female professors, including a Lise Meitner Professor, and one female senior lecturer. Of our 15 senior faculty members, five have an international background. Diversity, equity, and inclusion are important to us.

We are now seeking an internationally established researcher for a position as Senior Lecturer in distributed and/or large-scale optimization, learning, and control. We particularly welcome candidates operating at the intersection of at least two of these three areas.

The research may, for example, concern:

• scalable optimization methods

• resource-efficient and robust machine-learning methods

• optimization-based and/or learning-based methods for control

The research may be purely theoretical or motivated by a relevant application domain.

The position includes:

• conducting research of high international quality

• developing an independent line of research

• contributing to attracting external research funding

• teaching and supervising in optimization, learning, and/or control at the undergraduate, advanced, and doctoral levels

Subject

Automatic Control with focus on the intersection of optimization, learning, and control.

Subject Description

The subject area covers theory, methodology, and algorithm development, where research is conducted at the intersection between at least two of the following fields:

• optimization

• machine learning

• control theory

The research emphasizes large-scale and/or distributed methods and systems.

Central aspects include scalable methods with well-founded analysis, for example:

• computational and communication cost

• convergence and performance guarantees

• robustness

• handling of uncertainty and limited resources

The area includes, for example:

Large-scale optimization and machine learning

• Stochastic and/or (non-)convex optimization methods

• First-order methods

• Variance reduction

• Distributed and parallel optimization

• Federated learning

• Generalization/robustness and privacy aspects in scalable learning algorithms

Large-scale optimization and control

• Optimal control

• Model predictive control and other optimization-based control methods

• Distributed/coordination control

• Dynamic optimization

• Analysis and design of scalable algorithms with guarantees

Machine learning and control in large-scale systems

• Learning-based control

• Reinforcement learning and data-driven control methods

• Adaptive methods

• Safe/robust learning-based control

• Methodologies for stability, safety, and performance

Application-driven method development

For example in:

• energy systems

• communication systems

• robotics/autonomous systems

• socio-technical systems

• life sciences

Work Duties

Work duties include:

• Research within the subject area

• Teaching in the first, second and third cycles of studies

• Supervision of degree projects and doctoral students

• Actively seeking external research funding

• Collaboration with industry and wider society

• Administration related to the work duties listed above

Qualification Requirements

Appointment to Senior Lecturer requires that the applicant has:

• A PhD or corresponding research competence or professional expertise considered important with regard to the subject matter of the post and the work duties it will involve.

• Demonstrated teaching expertise.

• Completed five weeks of training in higher education teaching and learning, or acquired equivalent knowledge by other means, unless there are valid reasons.

Assessment Criteria

When assessing the applicants, special importance will be given to research and teaching expertise within the subject, with research experience weighted more strongly than teaching experience.

The following criteria must be fulfilled:

• A good national and international standing as a researcher.

• Good teaching ability, including the ability to conduct, develop and lead teaching and educational activities using a variety of teaching methods.

• Ability to supervise doctoral students to achieve a PhD.

• Ability to collaborate with wider society and communicate research activities.

• A general ability to lead and develop activities.

Additional Requirements

• Very good oral and written proficiency in English.

• Significant documented research experience (e.g., postdoc or doctoral studies) from another university/institute or relevant industry/public sector experience.

• Documented research experience at the intersection of at least two of the following areas:

  • optimization

  • machine learning

  • control

• Documented experience of successfully applying for and obtaining external research funding.

• Collaborative skills, initiative and the ability to solve work tasks independently.

Other Qualifications

• Documented experience of collaboration with industry and/or society.

• Documented experience of supervising doctoral students through to completion of their PhD.

The extent to which the applicant’s experience and skills complement and strengthen ongoing research, education, and departmental innovation will also be considered.

We Offer

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

Read more on the University website about being a Lund University employee: Work at Lund University.

Instructions on How to Apply

Applications shall be written in English.

Please prepare the application in accordance with LTH’s Academic Qualifications Portfolio.

Upload the application as PDF files in the recruitment system.

Read more: To apply for academic positions at LTH.

About LTH

LTH is Lund University’s Faculty of Engineering.

At LTH we:

• educate people

• build knowledge for the future

• contribute to the development of society

We create space for brilliant research and inspire creative advancements in technology, architecture and design.

LTH has nearly 12,000 students.

Each year:

• around 100 theses are published

• approximately 2,000 scientific findings are produced

Many research results and degree projects are also transformed into innovations.

Together we explore and create — to benefit the world.

We kindly decline all sales and marketing contacts.

Employment Details

Type of employment: Permanent position
Contract type: Full time
First day of employment: According to agreement
Salary: Monthly
Number of positions: 1
Full-time equivalent: 100 %
City: Lund
County: Skåne län
Country: Sweden
Reference number: PA2026/464

Contact

Karl-Erik Årzén
Phone: +46462228782
Email: karl-erik.arzen@control.lth.se

Union Representatives

SACO: Saco-s-rådet vid Lunds universitet
Phone: 046-2229364
Email: kansli@saco-s.lu.se

OFR/ST: Fackförbundet ST:s kansli
Phone: 046-2229362
Email: st@st.lu.se

SEKO: Seko Civil
Phone: 046-2229366
Email: sekocivil@seko.lu.se

Published: 10.Mar.2026
Last application date: 15.May.2026

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