The Faculty of Science and the Leiden Institute of Advanced Computer Science (LIACS) are looking for a:

PhD Candidate on Problem-aware and Similarity-driven Optimization Algorithm Design, Selection and Configuration

We are looking for an outstanding PhD candidate on Problem-aware and Similarity-driven Optimization Algorithm Design, Selection and Configuration. This research will draw upon established concepts within the optimization community, from Local Networks and Exploratory Landscape Analysis to Transfer Learning. The scope of the project ranges from benchmarking studies to the definition of appropriate similarity measures and their application for algorithm selection and configuration, addressing crucial societal challenges currently faced in industry, in fields like engineering design, to provide an example. Subsequently, the project will focus on the development of robust and automatically customizable algorithms.

This research is motivated by an undiscussed need for problem-aware thinking and algorithm design. Indeed, the efficiency of black-box optimization algorithms is intricately tied to the nature of the problem they aim to solve. While benchmarking serves as an initial stride in comprehending algorithmic behavior within specific problem settings, the challenge lies in understanding what benchmark characteristics are possessed by the real-world scenarios. This gap hinders the ability to make well-informed decisions about algorithm selection and hyperparameter configuration under specific circumstances. Bridging this divide between benchmarked performance and real-world problem characteristics is crucial for practitioners seeking to optimize outcomes and tailor algorithmic approaches to the complexities inherent in diverse, dynamic, practical challenges and environments.

Despite many techniques for autotuning algorithm settings available in Machine Learning, e.g., in the field of hyperparameter optimization, how to transfer these methods to practical problems characterized by highly expensive function evaluations, often relying on numerical simulation, is still open. In fact, automatic algorithm configuration can be described from a machine learning perspective as the problem of finding good parameter settings for solving unseen problem instances by learning on a set of training problem instances. In the real world, other instances of the problem under investigation are rarely available. This PhD project wants to fill this gap.

The execution of the research will be highly participatory. You will spend time at the Faculty of Science offices, particularly in the Natural Computing Cluster within the Leiden Institute of Advanced Computer Science. You will collaborate with fellow motivated PhD students within the cluster, as well as with developers and maintainers of the IOH Profiler Benchmarking platform.

Key responsibilities

• Conduct original and novel research in the field of landscape-aware optimization and machine learning for problem-dependent efficient optimization;
• Publish and present scientific articles at international journals and conferences;
• Active communication with other PhD candidates and members in the Natural Computing cluster.
• On-site presence (90% of working time) to facilitate spontaneous interactions with supervisors and colleagues and foster a vibrant and engaged community within the office space;
• Development and maintenance of the IOH Profiler Benchmarking platform;
• Assist in relevant teaching activities;

Selection Criteria

The successful applicant should be a motivated university graduate who is a top performer among his/her peers and has an excellent education and/or research track record proven by relevant experience, publications, etc. The applicant is expected to have:

• MSc degree in Artificial Intelligence, Data science, Applied Mathematics, Computer science, or related field;
• Good programming skills in e.g., Python, R, C++;
• Solid experience in Machine (Deep) Learning and/or black-box optimization;
• Interest in real-world applications, previous experience in applied fields (e.g., engineering design, fluid mechanics, bioscience, finance) is a plus;
• Excellent written and oral communication skills in English, Dutch proficiency or willingness to learn is a plus;
• Ability to work with diverse stakeholders, e.g., industry professionals, academic researchers.

Research at our faculty
The Faculty of Science is a world-class faculty where staff and students work together in a dynamic international environment. It is a faculty where personal and academic development are top priorities. Our people are committed to expand fundamental knowledge by curiosity and to look beyond the borders of their own discipline; their aim is to benefit science, and to make a contribution to addressing the major societal challenges of the future.

The research carried out at the Faculty of Science is very diverse, ranging from mathematics, information science, astronomy, physics, chemistry and bio-pharmaceutical sciences to biology and environmental sciences. The research activities are organized in eight institutes. These institutes offer eight bachelor’s and twelve master’s programs. The faculty has grown strongly in recent years and now has more than 2.800 staff and almost 6,000 students. We are located at the heart of Leiden’s Bio Science Park, one of Europe’s biggest science parks, where university and business life come together. For more information, see https://www.universiteitleiden.nl/en/science and https://www.universiteitleiden.nl/en/working-at

The Leiden Institute of Advanced Computer Science (LIACS) is the Artificial Intelligence and Computer Science Institute in the Faculty of Science of Leiden University. We offer courses at the Bachelor and Master of Science level in Artificial Intelligence, Computer Science, ICT in Business, Media Technology, and Bioinformatics. According to an independent research visitation, we are one of the foremost computer science departments of the Netherlands. We strive for excellence in a caring institute, where excellence, fun, and diversity go hand in hand. We offer a clear and inviting career path to young and talented scientists with the ambition to grow. For more information about LIACS, see https://www.cs.leiden.edu

Terms and conditions
We offer a full-time 1-year term position for initially one year. After a positive evaluation of the progress of the thesis, personal capabilities and compatibility the appointment will be extended by a further three years. Salary ranges from € 2.770, - to € 3.539, - gross per month (pay scale P in accordance with the Collective Labour Agreement for Dutch Universities).
Leiden University offers an attractive benefits package with additional holiday (8%) and end-of-year bonuses (8.3%), training and career development and sabbatical leave. Our individual choices model gives you some freedom to assemble your own set of terms and conditions. Candidates from outside the Netherlands may be eligible for a substantial tax break.

All our PhD students are embedded in the Leiden University Graduate School of Science https://www.universiteitleiden.nl/en/science/graduate-school-of-of-science. Our graduate school offers several PhD training courses at three levels: professional courses, skills training and personal effectiveness. In addition, advanced courses to deepen scientific knowledge are offered by the research school.

D&I statement
Diversity and inclusion are core values of Leiden University. Leiden University is committed to becoming an inclusive community which enables all students and staff to feel valued and respected and to develop their full potential. Diversity in experiences and perspectives enriches our teaching and strengthens our research. High quality teaching and research is inclusive.

Information
Enquiries can be made to Dr. Elena Raponi, e.raponi@liacs.leidenuniv.nl. If you have any questions about the procedure, please contact Anne-Marie Alleblas, email: jobs@liacs.leidenuniv.nl.

Applications
Please submit your application online via the blue button in the vacancy. Please ensure that you upload the following additional documents quoting the vacancy number:

• Motivation Letter;
• Curriculum Vitae;
• Two reference letters, by referees who have agreed to be contacted;
• GitHub personal page, if available;

Only applications received before April 15, 2024 can be considered.