IT / Telekommunikation

Mehrjährige Berufserfahrung

The University of Passau owes its strong visibility and good repute to excellent research, innovative teaching and its tight-knit international academic networks. Some 11,000 students and more than 1,300 staff study and work on our University campus, which is located a stone's throw from the historical Old Town of Passau and combines state-of-the-art technical infrastructure with award-winning architecture. Internationally successful high-tech companies and a vibrant start-up scene, coupled with a rich culture and Lower Bavarian traditions, give Passau and the surrounding area a special appeal that makes it a great place to live and work.

Postdoctoral Researcher (m/f/d) at the Chair of Mathematical Data Science

Passau
Unternehmen: Universität Passau

Aufgabe:

* Plan, administer and deliver classes of a total of approximately 5 teaching contact hours per week (either in German or English). * Collaborate on research regarding approximate sampling of distributions, analysis of Markov chain Monte Carlo methods, Bayesian inference and algorithmic design in general on the borderline between Machine Learning and mathematics. * Organise and support conferences/sessions/workshops and grant proposals.

Qualifikation:

* Must have a doctorate in mathematics, statistics, Machine Learning or a related field. o Strong theoretical background in at least one of the following areas: o Markov chain Monte Carlo o Learning theory o Numerical benchmarking

Wir bieten:

Remuneration in accordance with pay grade E13 of the German public-sector collective agreement TV-L; the salary step depends on your qualifications and experience (gross income about 61,000 Euro per annum). A rewarding, diverse and challenging academic position on a modern, verdant campus located on the banks of the river Inn, a few minutes' walk from the historic Old Town of Passau. A great work climate in a family-friendly environment.

Bewerben unter:
Universität Passau
Innstraße 41, 94032 Passau (GPS: 13.44, 48.56)

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