Phd Candidate for End-to-end Causal Learning
3 weken geleden
Vacancy number
15148
Job type
PhD positions
Hours (in fte)
1,0
External/ internal
External
Location
Leiden
Placed on
17 September 2024
Closing date
16 October 2024 29 more days to apply
The Faculty of Science, Leiden Institute of Advanced Computer Science, is looking for a
**Project description**
Causal inference is one of the primary goals in empirical sciences, including medicine, biology, economy, and physical science. For instance, researchers may seek to estimate the effects of smoking on lung cancer, education on salaries, or specific genes on phenotypes. If we have only access to observational data, a part of causal relations among the variables can be recovered, and hence, the investigator is commonly left with some (or in some cases many) unresolved causal relations. On the other hand, if we can perform experiments in the system, the causal graph representing the underlying system can be fully recovered from sufficient number of interventions. However, in many practical cases, performing each intervention might take days to complete or be too costly.
In this project, we aim to develop an end-to-end framework for the task of causal inference. This framework comprises three main components: causal discovery, identification analysis, and experiment design. The causal discovery part learns a causal model of the environment from the available data. This model is then used in the identification analysis to get an estimate of the causal effect. If the estimation is not accurate, a limited number of experiments are performed through the experiment design part to enhance knowledge about the underlying system, thereby improving the accuracy of causal effect estimation. This project combines techniques from statistics, graphical model learning, and stochastic optimization, embracing an interdisciplinary approach.
**Key responsibilities**
- Conduct original and novel research in the field of causal inference/discovery;
- Publish and present scientific articles in top ML venues;
- Contribute to educational activities;
- Write a dissertation;
- Assist in relevant teaching activities;
**Selection Criteria**
- A MSc degree in Computer Science, Statistics, Data Science, Artificial Intelligence, or a related field;
- Strong knowledge of and experienced with statistics, machine learning, and stochastic optimization;
- Creative, ‘making things work’ mentality, independent, and communicative team player;
- Experienced with writing scientific manuscripts and academic writing skills;
- Excellent programming skills (Python and ML packages);
- Interested in participating in educational activities;
- Excellent proficiency in English (oral and written);
**Research at our faculty**
**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 € 2.872;
- to € 3.670;
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.
**Work-life balance**
**Information**
**Applications**
- Curriculum vitae (CV);
- Grade list and MSc degree (or expected graduation date);
- (Draft of) MSc thesis;
- Contact information for two references;
- Link to public code repository (e.g., GitHub) or example of written code base.
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