Faculty of Engineering, LTH, The Department of Computer Science

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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 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 and hardware design.

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

Topics of interest:

  • Black-box optimization.
  • Derivative-free optimization (DFO).
  • Bayesian optimization.
  • Algorithm configuration and selection.
  • Active Learning.
  • Automated machine learning (AutoML).
  • Neural architecture search (NAS).
  • Hyperparameter optimization.
  • Learning to learn.
  • Meta learning and transfer Learning.
  • Reinforcement learning (RL).
  • Optimization of neural networks.
  • Evolutionary algorithms (EA).
  • Discrete optimization and NP-hard problem solving.
  • Data-driven analysis of algorithms, hyperparameter importance, etc.

Some applications of interest:
Image classification. Natural Language Processing (NLP). Design space exploration (DSE). Optimizing compilers. Hardware design.

Work duties
The main duties involved in a post-doctoral posistion is to conduct research. Teaching may also be included, but up to no more than 20% of working hours. The position includes the opportunity for three weeks of training in higher education teaching and learning.

The work duties may also involve supervision of degree projects and doctoral students, collaboration with industry and wider society, active seeking of research funding, and administration related to the work duties listed above.

Qualification requirements
Appointment to a post-doctoral position requires that the applicant has a PhD, or an international degree deemed equivalent to a PhD, within the subject of the position, completed no more than three years before the last date for applications. Under special circumstances, the doctoral degree can have been completed earlier.

Additional requirements:

  • Very good oral and written proficiency in English.

Assessment criteria and other qualifications
This is a career development position primarily focused on research. The position is intended as an initial step in a career, and the assessment of the applicants will primarily be based on their research qualifications and potential as researchers. Particular emphasis will be placed on research skills within the subject. For appointments to a post-doctoral position, the following shall form the assessment criteria:

  • A good ability to develop and conduct high quality research.
  • Teaching skills.

Other qualifications:

  • PhD thesis in statistical learning and/or computer vision, with focus on Black-box optimization and AutoML.
  • Good programming skills in Python or similar, knowledge of compiler technology and hardware accelerators are considered a merit.

Consideration will also be given to good collaborative skills, drive and independence, and how the applicant’s experience and skills complement and strengthen ongoing research within the department, and how they stand to contribute to its future development.

Terms of employment
This is a full-time, fixed-term employment of a maximum of 2 years. The period of employment is determined in accordance with the agreement “Avtal om tidsbegränsad anställning som postdoktor” (“Agreement on fixed-term employment as a post-doctoral fellow”) between Lund University, SACO-S, OFR/S and SEKO, dated 4 September 2008.

Instructions on how to apply
Applications shall be written in English. Please draw up the application in accordance with LTH’s Academic qualifications portfolio – see link below. Upload the application as PDF-files in the recruitment system.

To apply for academic positions at LTH

Type of employment Temporary position
Contract type Full time
First day of employment As soon as possible
Salary Monthly
Number of positions 1
Full-time equivalent 100
City Lund
County Skåne län
Country Sweden
Reference number PA2020/3117
  • Jacek Malec, jacek.malec@cs.lth.se
  • Luigi Nardi, luigi.nardi@cs.lth.se
Published 23.Nov.2020
Last application date 15.Mar.2021 11:59 PM CET

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