Are you interested in developing state-of-the-art statistical and machine learning techniques? Do you want your methods to be applicable to real-world complex data sets? Apply for this PhD position!

Your job
This PhD position is part of the project "Getting the best predictions from complex data sets: Balancing scalability and interpretability." In this data-driven era, high-dimensional data sets hold a wealth of untapped insights. The main challenge is to optimise predictive models from these complex data sets while preserving interpretability, a crucial aspect for subsequent application in fields such as behavioural prediction and medical diagnostics. This project aims to bridge the gap between the scalability of predictive modeling techniques and their interpretability by combining rigorous statistical methods with innovative machine learning approaches.

In this project, we will optimise Bayesian penalised regression methods to provide optimal predictions in high-dimensional data sets. In addition to these more traditional statistical methods, we will explore the use of learning techniques like Generative Adversarial Networks (GANs) and Adversarial Random Forests (ARFs) in complex real-world data sets. To enhance both predictive accuracy and interpretability, we will investigate how prior knowledge can best be incorporated in these methods. Finally, the developed methods will be extensively evaluated and compared, they will be applied to real-world complex data sets from the social and medical sciences, and they will be implemented in a user-friendly R-package.

You responsibilities will be the following:

• conducting the research (literature research, developing the methods, conducting simulation studies, analysing existing data sets, preparing and storing data packages for sharing);
• writing international scientific publications and a dissertation;
• giving presentations at (inter)national scientific conferences;
• active participation in the department of Methodology and Statistics (M&S);
• knowledge utilisation: collaborating and sharing findings with applied researchers and developing user-friendly software (in the form of an R-package);
• following courses/trainings (e.g., in the context of IOPS and the local graduate school).

Your work will also include between 10% and 20% teaching tasks. You will be well guided and supported by two daily supervisors as well as a senior supervisor from M&S.

We are looking for an enthusiastic colleague who meets the requirements below:

• a (completed or almost completed) Research Master’s degree in Methods & Statistics or in another relevant field such as computer science, mathematics, (bio)statistics with a demonstrable affinity for advanced statistics;
• the ambition to conduct excellent scientific research and are motivated to create impact with your research;
• a demonstrable affinity with statistics and machine learning techniques and you want to figure out how and why they work;
• a demonstrable affinity with and/or strong interest in computer programming;
• excellent verbal and written communication skills in English;
• good social, communicative and collaborative skills and you are well-organised.

We offer:

• a position in a collaborative, enthusiastic, and dedicated team, with guidance in your teaching and research tasks;
• a position for one year, with an extension to a total of four years upon a successful assessment in the first year, and with the specific intent that it results in a doctorate within this period;
• the position might be for 4,5 years, depending on the amount of teaching tasks;
• a working week of 38 hours and a gross monthly salary between €2,770 and €3,539 in the case of full-time employment (salary scale P under the Collective Labour Agreement for Dutch Universities (CAO NU);
• 8% holiday bonus and 8.3% end-of-year bonus;
• a pension scheme, partially paid parental leave, and flexible employment conditions based on the Collective Labour Agreement Dutch Universities.

In addition to the employment conditions from the CAO for Dutch Universities, Utrecht University has a number of its own arrangements. These include agreements on professional development, leave arrangements, sports and cultural schemes and you get discounts on software and other IT products. We also give you the opportunity to expand your terms of employment through the Employment Conditions Selection Model. This is how we encourage you to grow. For more information, please visit working at Utrecht University.

Universiteit Utrecht

A better future for everyone. This ambition motivates our scientists in executing their leading research and inspiring teaching. At Utrecht University, the various disciplines collaborate intensively towards major strategic themes. Our focus is on Dynamics of Youth, Institutions for Open Societies, Life Sciences and Pathways to Sustainability. Shaping science, sharing tomorrow.

The Faculty of Social and Behavioural Sciences is one of the leading faculties in Europe providing research and academic teaching in cultural anthropology, educational sciences, interdisciplinary social science, pedagogical sciences, psychology, and sociology. Almost 7,000 students are enrolled in a broad range of undergraduate and graduate programmes. The Faculty of Social and Behavioural Sciences has some 1,100 faculty and staff members, all providing their individual contribution to the training and education of young talent and to the research into and finding solutions for scientific and societal issues. The faculty is located at Utrecht Science Park near the historical city centre of Utrecht.