Future of Work, Data Science, and Applied Machine Learning
- Rare PhD opportunity for research on the Future of Work and Labour Markets applying Data Science and Machine Learning techniques
- Mentored by leading researchers and industrials in this space
- Access to datasets, strong industry partnerships, and international collaborations with top universities
The Research Topic
Advances in automation technologies and labour market shocks, such as COVID-19, have elevated the importance of labour mobility issues. Therefore, identifying viable and desirable job transition pathways for individuals has been a growing area of interest. The current state of the art methods [Moro, Frank et al, Nat.Comm.’21] [Dawson et al, PLOS ONE ‘21] take a global view to estimate the dynamics of labour markets using factors such as skill similarity, location, education, experience, and industry labour flows. While these have been shown as important features for explaining job transition pathways, they implicitly assume that the changes in the occupation of an individual depend solely on their current occupation. Other works [Kern et al, PNAS’19] have shown that worker psychometric profiles (measured using Big5 feature) closely relate to their occupations. In other words, people have predispositions towards certain occupations. This project aims to build personalised recommender systems for occupation transitions that account for worker personality, previous experience and education.
The Behavioral Data Science lab at UTS has several openings for PhD students to research the Future of Work and labour market networks. The topics of interest are job transitions, skills analysis, and the impacts of technology on labour markets. However, we’re open to other related areas, such as impacts of personality profiles on job choice, quantifying cultural differences between labour markets, and competitive dynamics of occupations and industries. The research will apply data science and machine learning techniques to labour market data, such as job ads and employment statistics.
What We Offer
This project is part of a wider collaboration with US-based universities and companies. This can open unique opportunities to research internships in both overseas academia and industry. Furthermore, you will be supported throughout your doctorate to become a first rate researcher. This will include:
- access to rich datasets to derive insights and real-world problems and know-how;
- exposure to industry networks to apply your work, and possible career paths upon graduation;
- collaboration with top international universities from our networks; and
- a supportive peer-group of doctoral researchers with regular events and reading groups.
- weekly meetings with supervisors to advance your research;
Once you graduate, you will have developed in-demand skills, published peer-review papers, broadened your professional network, and established yourself as an expert in the Future of Work.
To be successful in this role, you will either have:
- a strong background in data science and applied machine learning;
- an interest labour markets;
- knowledge of at least one programming language, such as Python and R;
- A strong background in economics and are willing to develop your technical skills;
You will also likely have an Honours or Masters degree. However, equivalent experience coupled with a relevant Bachelor’s Degree could also be sufficient.
Most importantly, you will show a great deal of initiative, thrive under autonomy, and have a passion for applying rigorous research to help people understand real problems.
Scholarships are available but will be awarded through a competitive process. Stipend top-ups and paid research work might be available throughout your thesis.
How to Apply
If you are interested, please send your CV and a cover letter to [email protected]. The cover letter should detail your academic track record, why you are interested in this topic and why you are a good match for the subject.
[Moro et al, Nat.Comm.’21] Moro, E., Frank, M. R., Pentland, A., Rutherford, A., Cebrian, M., & Rahwan, I. (2021). Universal resilience patterns in labor markets. Nature Communications, 12(1), 1972. https://doi.org/10.1038/s41467-021-22086-3
[Dawson et al, PLOS ONE ‘21] Dawson, N., Williams, M.-A., & Rizoiu, M.-A. (2021). Skill-driven Recommendations for Job Transition Pathways. PLOS ONE. Retrieved from http://arxiv.org/abs/2011.11801
[Kern et al, PNAS’19] Kern, M. L., McCarthy, P. X., Chakrabarty, D., & Rizoiu, M.-A. (2019). Social media-predicted personality traits and values can help match people to their ideal jobs. Proceedings of the National Academy of Sciences, 116(52), 26459–26464. https://doi.org/10.1073/pnas.1917942116