Job offer
Role details
Job location
Tech stack
Job description
The Junior Professorship in "Probabilistic Methods in Machine Learning" is at the interface of applied mathematics, statistics, and computer science. It is dedicated to the development and the analysis of modern machine learning methods, with a focus on probabilistic modelling that enables, for example, to account for uncertainties when training neural networks, to capture complex and high-dimensional models and data, and to derive robust and interpretable predictions from them.
The professorship is embedded in a dynamic research environment in a rapidly changing region and seeks collaborations both within BTU and with non-university research institutions such as DLR or Fraunhofer institutes. The successful candidate is expected to engage in existing and new research initiatives, including those involving different faculties.
We are looking for:
A strong researcher with a forward-looking, internationally competitive profile in the field of probabilistic methods in machine learning. Possible research focus areas include stochastic simulation and approximation methods (for example, Markov Chain Monte Carlo, interacting particle systems, PINNs) or stochastic optimization (e. g., stochastic gradient methods for high-dimensional neural networks, reinforcement learning, variational inference). In addition to methods development and theoretical research on modern AI and ML methods, the successful candidate is expected to incorporate innovative approaches into their research that connect classical probabilistic models with modern deep learning architectures. Examples include Bayesian deep learning or the computationally efficient implementation of generative diffusion models for specific applications. A concrete connection to BTU's profile lines is desired., Other duties result from the requirements set by § 44 BbgHG in conjunction with § 3 BbgHG.
The requirements and conditions for appointment are set out in §§ 47 and 48 BbgHG. According to § 47 Para. 2 BbgHG, the periods of full-time academic activity between the last examination performance of the doctorate and the application for a junior professorship may not exceed six years. These periods shall be extended to the extent of a reduction in working hours by at least one fifth of the regular working hours granted for the care or nursing of one or more children under the age of 18 or other relatives in need of care.
According to § 48 BbgHG, junior professors are appointed as temporary civil servants for a period of up to four years. If the interim evaluation is positive, the appointment is to be extended to a maximum of six years. After successful probation during the six-year junior professorship, there is the option, within the framework of the tenure track, to transfer a full professorship of grade W3 to the holder of the post after an appointment procedure has been carried out.
Requirements
In teaching, the junior professorship covers foundational and advanced topics in AI and data science within the MSc programs in Mathematical Data Science and Artificial Intelligence, while it also contributes to the undergraduate mathematics and computer science programs in the areas of probability theory and statistics, as well as to service teaching for other degree programs. This includes fostering early-career researchers through research-oriented teaching and supervision. Teaching will be carried out in both German and English. If the candidate does not yet have sufficient German language skills, they are expected to learn German soon, in order to be able to participate in the management of the institute, the faculty, and both university and non-university committees, as well as to teach in German-language bachelor's degree programs., * a completed university degree,
- teaching aptitude, and
- a special aptitude for academic work, generally evidenced by an outstanding doctoral degree.
Ideally, you also have experience in securing third-party funding and carrying out externally funded projects. Experience in DFG or EU projects is particularly welcome.
You are able to teach at all curricular levels, from bachelor's through doctoral studies, to supervise theses, and to support early-career researchers. Your knowledge and experience will also enable you to contribute to academic self-governance and to help shape the profile of the faculty., PhD or equivalent
Research Field Mathematics » Applied mathematics
Education Level PhD or equivalent
Specific Requirements
As the future junior professor, you can provide evidence of the following qualifications in accordance with § 47 (1) of the Brandenburg Higher Education Act (BbgHG):
- a completed university degree,
- teaching aptitude, and
- a special aptitude for academic work, generally evidenced by an outstanding doctoral degree.
Ideally, you also have experience in securing third-party funding and carrying out externally funded projects. Experience in DFG or EU projects is particularly welcome.
You are able to teach at all curricular levels, from bachelor's through doctoral studies, to supervise theses, and to support early-career researchers. Your knowledge and experience will also enable you to contribute to academic self-governance and to help shape the profile of the faculty.
Languages GERMAN
Level Excellent
Languages ENGLISH
Benefits & conditions
- fair and transparent appointment negotiations,
- attractive working conditions in a city with a high quality of life, located in relative proximity to Berlin, Dresden, and Leipzig
- a dynamically developing research hub
- support with relocation to the vicinity of your workplace,
- comprehensive guidance through the Dual Career Service and family support
- an attractive salary with a negotiable appointment benefit.