Associate Senior Lecturer in Machine Learning

Location
Lund (SE)
Posted
Wednesday, 20 November 2019
End of advertisement period
Monday, 25 November 2019
Ref
PA2019/2923
Academic Discipline
Engineering & Technology
Contract Type
Fixed Term
Hours
Full Time

Lunds universitet, LTH, Institutionen för reglerteknik  

Lund University was founded in 1666 and is repeatedly ranked among the world’s top 100 universities. The University has 40 000 students and 7 600 staff based in Lund, Helsingborg and Malmö. We are united in our efforts to understand, explain and improve our world and the human condition.

LTH forms the Faculty of Engineering at Lund University, with approximately 9 000 students. The research carried out at LTH is of a high international standard and we are continuously developing our teaching methods and adapting our courses to current needs. The position is part of the Wallenberg AI, Autonomous Systems and Software Program (WASP), Sweden's largest individual research program ever. WASP has the ambition to strengthen and develop Sweden’s knowledge and competence in the areas of AI and machine learning through an international recruitment program. These recruitments are not intended as a mechanism to strengthen Sweden’s existing faculty, but to attract new talent to Sweden. http://wasp-sweden.org

Subject Machine learning

Subject description

Machine learning is the discipline concerned with computer software that can learn autonomously. Machine learning, including both neural network-based approaches and mathematical statistics-based approaches, has become a driving force behind many recent breakthroughs in artificial intelligence. From speech recognition, image analysis, natural language understanding, machine translation, question and answering systems, protein folding, and even playing GO, machine learning is breaking records. These exciting developments have not gone unnoticed to industry either; every major company now have large teams of researchers working with machine learning.

Machine learning is a broad subject with connections to several departments at Lund University. For this reason, we therefore state three different subject directions, coupled to the three departments participating in the call. The final departmental affiliation will be decided in connection to the employment.

1. Machine learning for control. This direction is mainly concerned with theory and methodology for the use of machine learning in dynamic feedback systems. Examples of this are reinforcement learning and deep reinforcement learning. Also the use of control methodology, such as dynamic programming and optimization, for improved machine learning efficiency is of interest.

2. Machine learning in the mathematical sciences (including pure mathematics, applied mathematics, mathematical statistics and numerical analysis). This direction will focus on theoretical aspects of machine learning, such as limitations of machine learning, learning techniques under qualitative restrictions and dynamic optimisation techniques for high-dimensional non-stationary signals and also applications within image analysis and signal processing.

3. Machine learning within electrical and information technology. This direction is oriented towards signal processing and implementation and is focusing on machine learning for communication, identification and positioning. It is also of interest to include aspects arising in distributed systems or with resource-constrained devices in terms of limited bandwidth, memory, processing or battery capabilities.

Work duties

Employment as an associate senior lecturer is a career development position and aims for the holder to develop his or her independence as a researcher and educator. The work duties mainly involve research and teaching including supervision at undergraduate, graduate and doctoral levels. Included in the duties is also to actively participate in and to develop contacts with external funders, within academia as well as outside, and to contribute in efforts to attract external funding. The position shall include the opportunity for five weeks of training in higher education teaching and learning.

Qualification requirements

Appointment to associate senior lecturer requires that the applicant has a PhD degree or acquired corresponding research expertise.

Priority should be given to applicants who have completed their degree or acquired the corresponding expertise no more than five years before the expiration date of the call.

Additional requirements: Very good oral and written proficiency in English.

Assessment criteria

For appointments to associate senior lecturer, the following shall form the assessment criteria:

  • Research excellence proved by scientific publications in machine learning.
  •  International research experience
  • Potential to strengthen basic research in machine learning in Sweden
  • Relevant pedagogical skills and training
  • Capability to collaborate with academic and industrial partners
  • Competence in some core area of the departments
  • A good ability to develop and conduct research of high quality.

Other qualifications:

Consideration will also be given to how the applicant’s experience and skills complement and strengthen ongoing research, education and innovation within the department, and potential to contribute to its future development.

Terms of employment: This is a full-time, fixed-term employment of 5 years. The period of employment is determined in accordance with Chapter 4 Section 12a§ HEA.

Instructions on how to apply Applications shall be written in English.

Draw up the application in accordance with the Academic qualifications portfolio at LTH, se link below, and attach it as three PDF files (in the recruitment system). Read more here: http://www.lth.se/english/working-at-lth/to-apply-for-academic-positions-at-lth/

Promotion to Senior Lecturer in Machine Learning During the period of employment, an associate senior lecturer can apply for appointment to a permanent position as senior lecturer if he or she has the required qualifications listed below, and is deemed to be suitable.

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.

Additional requirements:

Very good oral and written proficiency in English. Good ability to cooperate. Independence and drive.

Assessment criteria:

When assessing the applicants, special importance will be given to research and teaching expertise. For appointments to senior lecturer, the following shall form the assessment criteria:

  • A good national and international standing as a researcher. The requirement for international experience shall be assessed with consideration to the character and traditions of the subject.
  • Good teaching ability, including a good ability to conduct, develop and lead teaching and other educational activities on different levels and using a variety of teaching methods.
  • An ability to supervise doctoral students to achieve a PhD.
  • An ability to collaborate with wider society and communicate his or her activities.
  • A good general ability to lead and develop activities.

Lund University welcomes applicants with diverse backgrounds and experiences. We regard gender equality and diversity as a strength and an asset. We kindly decline all sales and marketing contacts.

To apply, please click the button "Login and apply"

Type of employment Temporary position longer than 6 months

Contract type Full time

First day of employment As soon as possible

Salary Monthly Number of positions 1

Working hours 100 %

Contact Anders Rantzer, +46462228778 Karl-Erik Årzén, +46462228782 Daniel Sjöberg, +46462227511 Anders Heyden, +46462228531

Union representative SACO:Saco-s-rådet vid Lunds universitet, 046-222 93 64, kansli@saco-s.lu.se OFR/ST:Fackförbundet ST:s kansli, 046-222 93 62, st@st.lu.se

Last application date 25.Nov.2019 11:59 PM CET