Postdoc on Causal Machine Learning for Spatio-temporal Datasets

4 maanden geleden

Leiden, Nederland Universiteit Leiden Voltijd

The Faculty of Science and the Leiden Institute of Advanced Computer Science (LIACS) are looking for a:

Postdoc on Causal Machine Learning for Spatio-temporal Datasets

The is looking for an excellent Postdoc researcher (1.0 FTE) with a background in Computer Science (or a closely related field) to join a project focused on developing an advanced machine learning framework for spatio-temporal datasets. The position is for 4 years and is partially funded by the Dutch Research Council (NWO) through the Aspasia premium awarded to dr. Mitra Baratchi for her research on machine learning for spatio-temporal data. The successful applicant will be embedded in the and the research groups and collaborate with researchers at the Natural Computing (NACO) and the Explanatory Data Analysis (EDA) Research Groups. Moreover, there are plenty of opportunities for interaction and collaboration with other groups at the institute and internationally.

The research project is broadly focused on automated intervention design. An intervention is any external interference in an ongoing process that is performed to achieve a particular outcome. Being able to answer interventional questions automatically will help decision-makers solve complex challenges (i.e., reducing methane emissions or reducing the infection rate during a pandemic). Most machine learning algorithms designed for spatio-temporal data do not allow assessing the impact of interventions without capturing causal links. At the same time, configuring causal machine learning algorithms is extremely difficult, being an unsupervised learning task. In this project, we aim to design algorithmic solutions (e.g., new Automated Machine Learning approaches) for automatic and scalable assessment of interventions based on observational spatio-temporal datasets generated by modern sensing technologies (e.g., IoT sensors, mobile devices, Earth observations).


Key Responsibilities

Within this position, it is expected that you will:

conduct research within the scope of the project leading to peer-reviewed publications in journals and conference proceedings; collaborate closely with dr. Elena Raponi (the Natural Computing Group) and dr. Saber Salehkaleybar (the Explanatory Data Analysis Group) at LIACS; engage in teaching activities of the STAR Research Group and supervision of BSc and MSc students;


Selection Criteria

We are looking for a candidate with expertise or experience with one or more of the following topics: machine learning for spatio-temporal data, causal machine learning (casual discovery and causal inference), and automated machine learning.

holding (or close to acquiring) a PhD degree in Computer Science, Artificial Intelligence or a closely related field; strong research vision and an academic mindset; strong publication record; being able to collaborate with scientific peers inside and outside your own research area; strong programming skills in Python or other programming languages; excellent proficiency and communication skills in English;

Research at our faculty

Terms and conditions
We offer a full-time 4-year 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 € 3.226,- until € 5.090,- gross per month (pay scale 10 in accordance with the Collective Labour Agreement for Dutch Universities).
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.

D&I statement
Diversity and inclusion are core values of Leiden University. Leiden University is committed to becoming an inclusive community which enables all students and staff to feel valued and respected and to develop their full potential. Diversity in experiences and perspectives enriches our teaching and strengthens our research. High quality teaching and research is inclusive.

Applications
Please submit online your application via the blue button in the vacancy. Please ensure that you upload the following additional documents quoting the vacancy number:

A short cover letter detailing your motivation to apply for the position; A brief description of your research plan describing a research idea and research questions that you would want to work on in light of this particular position (maximum two A4 pages); An academic CV; Ph.D. thesis when appropriate; Link to key publications; The names and addresses of two persons who can be contacted for reference (who have agreed to be contacted);


Only applications received before 30 September 2024 can be considered.

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