PostDoc fellowships for ML, AutoML, or RecSys for 2-3 years in Siegen (Germany) and/or anywhere in the world

The University of Siegen successfully received funding from the Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 945422. The funding will be used to offer a number of postdoc fellowships,

We — the Intelligent Systems Group — is a potential host for 1 or even 2 postdoc fellows. Specifically, there are two options for a postdoc fellow:

Incoming fellows will work with us for 2 years here at the University of Siegen. They are fully employed by the university including pension, health insurance, etc. Your net salary will be roughly between 2700€ and 3200€ per month.

Outgoing postdoc fellows will be employed by the University of Siegen for 3 years, BUT the first 2 years they work at a university of their choice outside of Germany. Only during the third year, the postdoc fellow will work in Siegen. However, during the entire three years, you will work closely with your host in Siegen (e.g. us, the Intelligent Systems Group). Outgoing postdocs do not need to be from Siegen, and not even from Germany. Anyone with any nationality can apply (some restrictions apply).

If you are interested in working with us, in either scheme, please check the University of Siegen’s STAR project page, especially the eligibility criteria. Then contact us, to discuss if we are interested in sponsoring your fellowship (our group needs to provide some financial contribution to your salary). As a final step, you would then apply for the fellowship.

Please note, especially for the outgoing fellowship, you would need to provide strong evidence of independent and successful research work. Suitable evidence would include first-author publications at top machine learning or recommender systems venues, at least some small research grants, successful student supervision (evidenced through joint publications), and a very clear research vision for the next 2-3 years. Ideally, you would be interested in AutoML and recommender systems, but we are open to discussing any topic that relates to machine learning or recommender systems.