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Work assignments 

You will join the research project Countering Bias in AI Methods in the Social Sciences, a collaboration between the Institute for Analytical Sociology (IAS) at Linköping University and the Department of Computer Science and Engineering (CSE) at Chalmers University of Technology, funded by WASP-HS. The employment is at IAS. The project sits at the intersection of causal inference and natural language processing.

A central challenge when drawing causal conclusions from observational data is adjusting for confounding factors. In the social sciences, many of these factors are not measured directly but are instead recorded in unstructured text. Using text to adjust for confounders is promising, yet it introduces a specific risk that we call treatment leakage: when the text also carries information about the treatment assignment, conditioning on it can bias the causal estimate. Your task is to collaborate with our team at CSE that is leading the methods development and then apply their methods to applied social science questions. Their methods let researcher use textual data for causal inference while detecting and removing this source of bias.

The work combines modern natural language processing with causal estimation. Recent directions in the project include using large language models to remove treatment-predictive information from text, benchmarking debiasing estimators such as Design-based Supervised Learning and Prediction-Powered Inference, and applying mechanistic interpretability techniques, for example sparse feature circuits and the SHIFT method, so that text classifiers ignore features related to treatment assignment.

You will help shape the direction of this research on the social science side, lead your own studies, publish in leading venues, and collaborate closely with the project team, including co-supervising the project's PhD student and working with partners at the University of Texas at Austin. In terms of study area, you will primarily focus on estimating impact of various development (e.g., IMF, World Bank, UNDP, and Chinese) projects in Africa, using policy text and other modalities. Secondarily, other areas of applications with similar relevance to the leakage problem.

As postdoc, you will principally carry out research. A certain amount of teaching may be part of your duties, up to a maximum of 20% of working hours.

Qualifications

To be qualified to take employment as postdoc, you must have been awarded a doctoral degree or have a foreign degree that is deemed to be equivalent to a doctoral degree.  This degree must have been awarded at the latest by the point at which LiU makes its decision to employ you.

It is considered advantageous if your doctoral degree is no older than three years at application deadline for this job. If there are special reasons for having an older doctoral degree – such as taking statutory leave – then these may be taken into consideration.

We are looking for someone with a doctoral degree in social sciences and computational social science, or a closely related quantitative field, together with strong programming skills, for example in Python or R.  You are welcome to apply should you have a degree in applied parts of statistics, computer science, machine learning, or natural language processing, focusing on AI for Social Good or similar. Excellent written and spoken English is required, since the project is carried out in an international research environment. We also think you have solid training in at least one of the project's two pillars, causal inference or natural language processing, and a genuine interest in bridging the two. Experience in any of the following counts as a strong merit: causal inference with observational data, text as data, large language models, mechanistic interpretability, or semiparametric and design-based estimation. A record of publications at leading venues in social science, machine learning, natural language processing, statistics, or computational social science is highly meritorious.

You are a self-driven and collaborative researcher who enjoys working across disciplinary boundaries, takes independent responsibility for a research agenda, and communicates clearly with both computer scientists and social scientists.

The workplace

You can read about the workplace here at the Institute for Analytical Sociology (IAS): The Institute for Analytical Sociology. At IAS you will work in Professor Adel Daoud's group on artificial intelligence and causal inference for the social sciences, in close collaboration with the Data Science and AI division at Chalmers.

The employment

This employment is a temporary contract of two years with the possibility of extension up to a total maximum of three years. The employment is full-time. Starting by agreement.

Background screening may come to be carried out before any decision on employment is made.

Salary and employment benefits

At Linköping University, salaries are set individually and differentiated, in accordance with the applicable collective agreements (RALS/RALS-T) and the university’s salary-setting guidelines.

More information about employee benefits is available here.

Union representatives

Information about union representatives, see Help for applicants.

Application procedure

Apply for the position by clicking the “Apply” button. Your application must be received no later than September 24, 2026. 
 
Applications and documents received after the date above will not be considered. 
 
Please attach your selected research publications electronically, in pdf or word format, in the application template. Research publications, e.g. monographs, which cannot be sent electronically should be sent in three sets by mail to the University Registrar at Linköping University, University Registrar, S-581 83 Linköping, Sweden. The publications must be received by Linköping University no later than the deadline for application. 

Please note that printed publications will not be returned. They will be archived at Linköping University. 
 
In the event of a discrepancy between the English translation of the job announcement and the Swedish original, the Swedish version shall take precedent.