Job offer

Progress in statistics and machine learning research has led to great strides in using data to uncover relationships between things in the world we observe around us. Many questions of scientific and societal (e.g. medical) importance, however, are causal questions. Causal questions ask what the effects of new actions will be, rather than how things are related in a world in which we keep things as is.

Given the importance of answering such causal questions, many data-driven methods have been developed to estimate causal effects in machine learning as well as statistics, epidemiology, econometrics, psychometrics, and many others. However, these methods all rely on causal assumptions, such as positivity and unconfoundedness. When such assumptions are violated, these methods will return incorrect causal answers, which leads to flawed and potentially even harmful decision-making. Currently, however, there is a lack of data-driven methods to detect assumption violations, leading to problems that go undetected, and ultimately, untrustworthy causal claims.

In this project, our goal is to better understand the limits of the detection of causal assumption violations, develop new detection methods, incorporate these violations into causal models, and design procedures to mitigate their influence. By offering these tools to practitioners, we will contribute to a new, safer approach to addressing causal questions that leads to more trustworthy causal answers.

The project particularly focusses on positivity violations, that is the assumption that it is possible for each treatment of interest to have occurred for each relevant unit in the data. We are interested in developing methods to detect whether this assumption is violated, to find out for which treatments or subpopulations it holds, and to properly account for the uncertainty caused by limited support in the data. We want to investigate this in both simple and more complicated (e.g. high-dimensional or longitudinal) settings.

The project is part of the "Safe Causal Inference" consortium, a multi-disciplinary (computer science, mathematics, biostatistics, epidemiology) consortium spanning eight PhD positions to improve the trustworthiness of causal methods. Through the consortium, you will be able to collaborate with experts and peers from multiple causal inference disciplines to strengthen and inspire your work., Doctoral candidates will be offered a 4-year period of employment in principle, but in the form of 2 employment contracts. An initial 1,5 year contract with an official go/no go progress assessment within 15 months. Followed by an additional contract for the remaining 2,5 years assuming everything goes well and performance requirements are met.

We are looking for an enthusiastic and self-motivated person with a passion for research. We offer a stimulating scientific research environment and the embedding in a multi-disciplinary consortium that allows you to develop skills that go beyond discipline-specific boundaries. You will be supervised by Jesse Krijthe (computer science, TU Delft) and Nan van Geloven (biostatistics, Leiden University Medical Center) and be primarily based in the Pattern Recognition and Bioinformatics group of TU Delft, which includes researchers working on the methodology of machine learning, bioinformatics, computer vision, and socially perceptive computing., * A critical thinker with an open mind

• Strong interest in understanding and developing statistical and machine learning methods
• Master's degree in a relevant quantitative field (such as statistics, computer science, mathematics, econometrics, psychometrics, physics, etc.)
• Ability to program in Python, R, Julia or a similar (scientific) programming language
• Proficient in spoken and written English, Doing a PhD at TU Delft requires English proficiency at a certain level to ensure that the candidate is able to communicate and interact well, participate in English-taught Doctoral Education courses, and write scientific articles and a final thesis. For more details please check the Graduate Schools Admission Requirements.

Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities, increasing from €3059 - €3881 gross per month, from the first year to the fourth year based on a fulltime contract (38 hours), plus 8% holiday allowance and an end-of-year bonus of 8.3%.

As a PhD candidate you will be enrolled in the TU Delft Graduate School. The TU Delft Graduate School provides an inspiring research environment with an excellent team of supervisors, academic staff and a mentor. The Doctoral Education Programme is aimed at developing your transferable, discipline-related and research skills.

The TU Delft offers a customisable compensation package, discounts on health insurance, and a monthly work costs contribution. Flexible work schedules can be arranged.

Delft University of Technology is built on strong foundations. As creators of the world-famous Dutch waterworks and pioneers in biotech, TU Delft is a top international university combining science, engineering and design. It delivers world class results in education, research and innovation to address challenges in the areas of energy, climate, mobility, health and digital society. For generations, our engineers have proven to be entrepreneurial problem-solvers, both in business and in a social context. At TU Delft we embrace diversity as one of our core values and we actively engage to be a university where you feel at home and can flourish. We value different perspectives and qualities. We believe this makes our work more innovative, the TU Delft community more vibrant and the world more just. Together, we imagine, invent and create solutions using technology to have a positive impact on a global scale. That is why we invite you to apply. Your application will receive fair consideration. Challenge. Change. Impact! Faculty of Electrical Engineering, Mathematics and Computer Science The Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) brings together three scientific disciplines. Combined, they reinforce each other and are the driving force behind the technology we all use in our daily lives. Technology such as the electricity grid, which our faculty is helping to make completely sustainable and future-proof. At the same time, we are developing the chips and sensors of the future, whilst also setting the foundations for the software technologies to run on this new generation of equipment - which of course includes AI. Meanwhile we are pushing the limits of applied mathematics, for example mapping out disease processes using single cell data, and using mathematics to simulate gigantic ash plumes after a volcanic eruption. In other words: there is plenty of room at the faculty for ground-breaking research. We educate innovative engineers and have excellent labs and facilities that underline our strong international position. In total, more than 1000 employees and 4,000 students work and study in this innovative environment.