Sort by
Refine Your Search
-
Listed
-
Country
-
Field
-
of Vienna). About the position: Lead the research group focusing on hybrid quantum algorithms, quantum neuromorphic computing, and quantum machine learning Build your own team (PhD students, postdocs) 4-year
-
lit. b (postdoc) Limited until: 30.04.2029 Reference no.: 5346 The Faculty of Mathematics at the University of Vienna is the largest Austrian institution of mathematical research and tertiary education
-
engineer will contribute to the development, optimization, and operation of advanced microscopy devices, notably High-speed AFM and AFM-IR (atomic force microscopy coupled with infrared spectroscopy) and
-
sequencing-based assays, new genomic method development, perform molecular biology experiments and coordinate lab research. The candidate needs to have a Ph.D. Degree in molecular biology and previous postdoc
-
. B1 lit. b (postdoc) Limited contract until: 30.04.2029 Job ID: 5346 The Faculty of Mathematics at the University of Vienna is the largest Austrian institution of mathematical research and tertiary
-
de novo compound design. Is committed to providing relevant bioinformatic guidance, coaching and/or training of researchers, e.g. PhD students and postdocs, from the different research groups
-
: https://remik24-web.github.io/QT-website/ The candidates are welcome to inquire about the project details, research agenda and organizational issues. The questions should be sent by email to R. Augusiak
-
integration, and system integration for next-generation wireless systems. We envision programmable and intelligent cellular networks that can be dynamically and algorithmically instrumented and optimized
-
) Positions Postdoc Positions Application Deadline 20 Mar 2026 - 12:00 (Europe/Paris) Country France Type of Contract Temporary Job Status Full-time Hours Per Week 37h30 Is the job funded through the EU
-
- Knowledge of mathematical probability and statistics, and optimization methods - Knowledge of machine learning, including supervised and unsupervised learning, deep learning, and model evaluation - Knowledge