PhD-TA Exceptional Model Mining on time-varying data
Updated: 26 Oct 2023
The Data and AI cluster at Eindhoven University of Technology (TU/e) is searching for candidates for one fully-funded 5-year PhD-TA position. Of the time in the position, 75% will be spent on fundamental scientific research: the primary goal is to enrich the portfolio of available data mining techniques with new innovative methods. The remaining 25% of the time in the position will be spent contributing to the teaching duties of the Data and AI cluster. Hence, this position is an ideal springboard for an academic career: during your PhD trajectory, in which you will contribute fundamental advances to data mining research, you will also build up a portfolio of academic teaching experience. Upon successful completion of this position, your CV will be very attractive for universities with open tenure-track positions.
The scientific framework in which the position is grounded is Exceptional Model Mining (EMM), a data mining framework that strives to find subgroups within a dataset that are interesting. Subgroups are only deemed interesting if they satisfy two conditions. On the one hand, they must be interpretable: we must be able to succinctly describe the definition of a subgroup, so that the knowledge that they represent becomes actionable. On the other hand, they must be exceptional: they must display some kind of behavior that sets them apart from overall behavior. The scientific challenges revolve around how to efficiently search for subgroups, and how to express exceptional behavior such that the subgroups we find are meaningful.
Making an efficient traversal of the combinatorially-large subgroup search space is already challenging in traditional, cross-sectional data. In this PhD position, the main research challenge is to create efficient algorithms to search for exceptional subgroups in data that varies over time. The problem space encompasses both heuristic algorithms, such as Monte-Carlo Tree Search and Pattern Sampling, and exhaustive algorithms, where we will investigate Optimistic Estimates, GP-Growth, and Significant Pattern Mining. We may also look into whether the anytime algorithm with guarantees for Subgroup Discovery, called 'Refine&Mine', can be expanded to EMM settings. Completely fresh algorithmic ideas to tackle EMM are also most welcome. Please consult the PDF-document for further details about the project description encompassing this PhD position.
TU/e is a dynamic, research-intensive university in the heart of Europe. TU/e is consistently ranked within the top-100 positions in several world rankings for its research and quality of education. The Data and AI cluster consists of 18 professors, 1 lecturer, 3 postdoctoral researchers, 41 PhD students, and 7 research engineers. The research results of the cluster appear in top-tier conferences and journals including but not limited to SDM, KDD, ECML PKDD, ICDM, AAAI, DAMI, MACH, and KAIS. The cluster has close collaboration with several leading companies, organizations, and hospitals.
The successful candidate will be based in the Data Mining group of the DAI cluster, where opportunities are available to collaborate with other PhDs and to co-supervise MSc students on topics relevant to your research. Your research will be concluded with a PhD thesis. You will be supervised by dr. Wouter Duivesteijn and prof. dr. Mykola Pechenizkiy.
a solid background in computer science with specialization in data mining and/or machine learning; a minor in mathematics is a plus;
- A strong interest in data mining research with focus on local pattern mining, exceptional model mining, or related techniques.
- Data mining software development skills in at least one language, e.g. R, Python, Java.
- Good communication skills in English, both in speaking and in writing.
- Capability and willingness to work both independently and in a team of data scientists, and interact with domain experts; being highly motivated, rigorous, and disciplined.
- Being enthusiastic about working on challenging use cases.
- Affinity with contributing to teaching data mining at a university level; teaching experience will be considered an additional advantage.
- Experience in research and a publication record will be considered additional advantages.
A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station. In addition, we offer you:
- Full-time employment for five years, with an intermediate evaluation (go/no-go) after nine months. You will spend 25% of your employment on teaching tasks.
- Salary and benefits (such as a pension scheme, paid pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labour Agreement for Dutch Universities, scale P (min. €2,770 max. €3,539).
- A year-end bonus of 8.3% and annual vacation pay of 8%.
- High-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At TU/e we challenge you to take charge of your own learning process.
- An excellent technical infrastructure, on-campus children's day care and sports facilities.
- An allowance for commuting, working from home and internet costs.
- A Staff Immigration Team and a tax compensation scheme (the 30% facility) for international candidates.
38 hours per week
De Rondom 70