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PhD Scholarship in Machine Learning at Lund University

A new PhD position in computer science is available at Lund University. This position is available for 5 years. Potential candidates should apply before January 13, 2020.

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.

Subject description

Black box optimization concerns the optimization of functions that can only be evaluated through numerical simulation and in which partial derivates are either not known or not defined. One way to optimize these functions consists of using consecutive function evaluations to build and refine a surrogate model, and then use this model to drive the optimization. Albeit very effective, this model-based approach is typically computationally expensive, becoming impractical when the number of input variables is greater than a few dozens and when multiple objectives are considered jointly.

This project, financed by WASP (Wallenberg AI, Autonomous System and Software Programme, (http://wasp-sweden.org), aims at introducing innovative algorithms and methodologies to overcome the limitations of multi-objective black-box optimization. The research topic falls at the crossroads of the broader fields of black-box optimization and statistical machine learning. This project will develop statistical methods for modelling surrogate models. These models can then be queried using Bayesian optimization or similar techniques to identify values of process settings that optimize the quality of the multiple objectives, or that maximize the information gained from experiments. Using a Bayesian statistical approach augmented with user prior knowledge will enable combining information from different sources, e.g. experiments of different kinds, while accounting for the uncertainty involved in a rigorous and coherent manner.The new algorithms and methodologies will be tested on a variety of synthetic and real-world applications such as automated machine learning (AutoML), automated configuration of compilers, hardware design and computer vision.

This project is part of a collaboration with Stanford University. The student will be encouraged to apply to the WASP exchange program with Stanford to work closely with collaborators.

Work duties

The main duties of doctoral students are to devote themselves to their research studies which includes participating in research projects and third cycle courses. The work duties can also include teaching and other departmental duties (no more than 20%).

Admission requirements

Assessment criteria

Selection for third-cycle studies is based on the student's potential to profit from such studies. The assessment of potential is made primarily on the basis of academic results from the first and second cycle.

Consideration will also be given to good collaborative skills, drive and independence, and how the applicant, through his or her experience and skills, is deemed to have the abilities necessary for successfully completing the third cycle programme.

Only those admitted to third cycle studies may be appointed to a doctoral studentship. Third cycle studies at LTH consist of full-time studies for 4 years. A doctoral studentship is a fixed-term employment of a maximum of 5 years (including 20% departmental duties). Doctoral studentships are regulated in the Higher Education Ordinance (1993:100), chapter 5, 1-7 .

Instructions on how to apply

Applications shall be written in English and include a cover letter stating the reasons why you are interested in the position and in what way the research project corresponds to your interests and educational background. The application must also contain a CV, degree certificate or equivalent, and other documents you wish to be considered (grade transcripts, contact information for your references, letters of recommendation, etc.).


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