Postdoc in Machine learning applied to social and political sciences
Vor 3 Tagen
The Swiss Data Science Center (SDSC) is a strategic focus area of the ETH domain, with EPFL and ETH Zurich as founding partners, developing into a National Research Infrastructure in 2025. Its mandate is to support academic labs, hospitals, industry and public sector stakeholders, including cantonal and federal administrations, through their entire data science journey, from the collection and management of data to machine learning, AI, and industrialization.
The Center comprises a multi-disciplinary team of data and computer scientists and experts in selected domains, with offices in Zürich, Lausanne, and Villigen.
Project BackgroundWe are looking for a postdoc in machine learning and natural language processing, with experience in the application of these methods to the fields of social and political sciences.
You will work on a recently funded SNF project entitled “Measuring Political Performance”, where we aim to develop unbiased and robust measures of political achievement. You will work at the Swiss Data Science Center, which will allow you to interact with experts in AI and ML working in diverse fields.
We will aim to implement methods to measure and track political performance and legacy in the context of the Swiss parliament. The final goal is to devise mechanisms that allow us to further understand the political process, and how the policies implemented impact citizens’ lives. The measures are to be presented to the (Swiss) public via a designated webpage, thus increasing the impact of our research.
The SNF project is conducted in collaboration with political scientists at UZH. This interdisciplinary environment ensures the impact of the project from the point of view of the developed ML methods, and their application to these domain fields.
Main Duties and Responsibilities- Implement parsing and analysis methods to collect and extract relevant information for downstream tasks.
- Collaborate with political scientists to develop unbiased metrics to measure political success, performance, and impact.
- Develop new methods, create software packages, present and discuss the methods at scientific conferences and meetings, and ensure broad use of new methods through tutorials, rich documentation, and/or help-files.
- Implement general and intuitive methods that ensure broader usage by domain scientists.
- Engage with the domain science community to better understand their needs and procedures.
We are looking for a researcher with experience working in interdisciplinary teams, where the goals are not only implementing new methods but also their application in downstream problems in the domain fields. Ideally, the candidate has experience in, or a strong interest in learning, technologies for the presentation of these results in an amenable and intuitive manner, e.g., dashboards, web apps, scientific visualizations. This ensures the broad impact of our project, beyond the scientific community.
Profile- You have a PhD in computer science, machine learning, or data science, and have worked in NLP, using both traditional and LLM-based methods.
- You are used to working in interdisciplinary environments, collaborating with domain scientists, and are curious about learning from other non-technical disciplines.
- You enjoy crafting sophisticated software solutions, consisting of intuitive UIs that allow interaction with modern ML models.
- You have experience with Python and standard database technologies such as SQL and Neo4J. Besides, you have experience, or are interested in learning, UI technologies like React, Reflex, Gradio, Flask, etc.
- You know how to present complex results as appealing and informative visualizations.
- A stimulating, collaborative, cross-disciplinary environment in a world-class research institution, where you will be part of a team of 40 data scientists from more than 15 different countries, seeking to apply novel ML methods to solve real-world problems.
- We value work-life balance and consider part-time employment options as well as home-office work.
- Our work hours are flexible, and scheduling holidays is uncomplicated.
- Budget for travel and conference attendance.
- Support from research students hired in the project for the development of the methods.
- We encourage experimentation and creativity by actively promoting learning of new technologies and approaches on the job.
In line with our values, ETH Zurich encourages an inclusive culture. We promote equality of opportunity, value diversity, and nurture a working and learning environment in which the rights and dignity of all our staff and students are respected. Visit our Equal Opportunities and Diversity website to find out how we ensure a fair and open environment that allows everyone to grow and flourish.
Curious? So Are We.Please note that we exclusively accept applications submitted through our online application portal, and the process will be open for submissions until January 14th.
Applications via email or postal services will not be considered.
Please, with the application attach the following documents:
- CV
After receiving your application, we will do a first pre-screening and contact you with either a positive or negative answer. If successful, one of us will talk to you to inform you about the following steps and the selection process.
Further information about SDSC can be found on our website, including some examples of projects carried out by the academic team.
Questions regarding the position should be directed to hr@datascience.ch (no applications).
ETH Zurich is one of the world’s leading universities specializing in science and technology. We are renowned for our excellent education, cutting-edge fundamental research, and direct transfer of new knowledge into society. Over 30,000 people from more than 120 countries find our university to be a place that promotes independent thinking and an environment that inspires excellence. Located in the heart of Europe, yet forging connections all over the world, we work together to develop solutions for the global challenges of today and tomorrow.
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