PhD candidate for End-to-End Causal Learning (1.0fte) | |
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Workplace | Leiden, South Holland, Netherlands |
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Vacaturenr. 15148 Functie-categorie PhD-posities Omvang (fte) 1,0 Extern/intern Extern Locatie Leiden Geplaatst op 17 september 2024 Sluitingsdatum 16 oktober 2024 Nog 29 dagen om te solliciteren Solliciteer direct The Faculty of Science, Leiden Institute of Advanced Computer Science, is looking for a PhD candidate for End-to-End Causal Learning (1.0fte) 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. The candidate will be embedded in the Explanatory Data Analysis group at the Leiden Institute of Advanced Computer Science. More information about the group can be found at ?url=https%3A%2F%2Feda.liacs.nl%2F&module=jobs&id=20474" target="_blank" rel="nofollow">?url=https%3A%2F%2Feda.liacs.nl%2F&module=jobs&id=20474" target="_blank" rel="nofollow">https://eda.liacs.nl/ Key responsibilities
Selection Criteria
Research at our faculty The Faculty of Science is a world-class faculty where staff and students work together in a dynamic international environment. Our people are driven by curiosity to expand fundamental knowledge and to look beyond the borders of their own discipline. The research carried out at the Faculty of Science is diverse, ranging from mathematics, artificial intelligence, computer science, astronomy, physics, chemistry and bio-pharmaceutical sciences to biology and environmental sciences. The faculty has grown strongly in recent years and now has more than 1,300 staff and almost 4,000 students. We are located at the heart of Leiden’s Bio Science Park, one of Europe’s biggest science parks, where university and business life come together. For more information, see ?url=https%3A%2F%2Fwww.science.leidenuniv.nl&module=jobs&id=20474" target="_blank" rel="nofollow">?url=https%3A%2F%2Fwww.science.leidenuniv.nl&module=jobs&id=20474" target="_blank" rel="nofollow">https://www.science.leidenuniv.nl . The Leiden Institute of Advanced Computer Science (LIACS) is the Artificial Intelligence and Computer Science Institute in the Faculty of Science of Leiden University. We offer courses at the Bachelor and Master of Science level in Artificial Intelligence, Computer Science, ICT in Business, Media Technology, and Bioinformatics. According to an independent research visitation, we are one of the foremost computer science departments of the Netherlands. We strive for excellence in a caring institute, where excellence, fun, and diversity go hand in hand. We offer a clear and inviting career path to young and talented scientists with the ambition to grow. For more information about LIACS, see ?url=https%3A%2F%2Fwww.cs.leiden.edu&module=jobs&id=20474" target="_blank" rel="nofollow">?url=https%3A%2F%2Fwww.cs.leiden.edu&module=jobs&id=20474" target="_blank" rel="nofollow">https://www.cs.leiden.edu 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,- gross per month (pay scale P in accordance with the Collective Labour Agreement for Dutch Universities). All our PhD candidates are embedded in the Leiden University Graduate School of Science ?url=https%3A%2F%2Fwww.universiteitleiden.nl%2Fen%2Fscience%2Fgraduate-school-of-science&module=jobs&id=20474" target="_blank" rel="nofollow">?url=https%3A%2F%2Fwww.universiteitleiden.nl%2Fen%2Fscience%2Fgraduate-school-of-science&module=jobs&id=20474" target="_blank" rel="nofollow">https://www.universiteitleiden.nl/en/science/graduate-school-of-science 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. Leiden University offers an attractive benefits package with additional holiday (8%) and end-of-year bonuses (8.3 %), training and career development and sabbatical leave. Our individual choices model gives you some freedom to assemble your own set of terms and conditions. Candidates from outside the Netherlands may be eligible for a substantial tax break. Work-life balance Information Applications To apply for this vacancy, submit online your application via the blue button in the vacancy. Please ensure that you attach the following additional documents:
Only applications received before the 16th of October 2024 can be considered. Applicants may be contacted with requests for additional information and/or exploratory conversations before the closing date. Solliciteer direct
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