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The University of Rostock offers a diverse, varied and challenging position in a tradition-conscious, yet innovative, modern and family-friendly university in a lively city by the sea.

At the Faculty of Computer Science and Electrical Engineering, Institute for Visual and Analytic Computing, subject to allocation of funds, we are filling the following position as of 01.04.2026 on a temporary basis for the duration of the project BehAIve ending on 31.03.2029:

Research Assistant (m/f/d) - AI

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Start date
as of 01.04.2026
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Working hours
 full-time with 40 hours
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Remuneration pay group 13 TV-L
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Location Campus Südstadt
Tender number
 P 15/2026
Limitation limited until 31.03.2029
Application time 2026-02-18

Please do not hesitate to contact us for further information:

HR department:

Pia-Lucy Dahl
Phone number: 0381/498-1291
E-mail: pia.dahl@uni-rostock.de

Department:

Prof. Thomas Kirste
Phone number: 0381/498-7510
E-mail: thomas.kirste@uni-rostock.de

The BehAIve project is a major consortium funded by the Ministry of Science, Culture, Fedaral and European Affairs of the state of Mecklenburg–Western Pomerania. Objective of BehAIve is the development of an AI-based, situation-adaptive assistance system that supports elderly people in everyday activities. By integrating knowledge-based probabilistic time-series analysis, symbolic artificial intelligence, and deep learning into a holistic end-to-end system, the project aims to enhance quality of life for elderly individuals while sustainably relieving care professionals, with relevance both regionally and beyond. BehAIve consortium members are the University of Greifswald, University of Rostock, University Medicine Greifswald, University of Applied Science Wismar, and the German Center for Neurodegenerative Diseases.

We invite a highly qualified early career researcher to join our research group for Hybrid Methods in Artificial Intelligence and Machine Learning (HAIML). Objective of the advertised position is the development of hybrid neuro-symbolic and neuro-probabilistic models for the analysis of dynamic system within the BehAIve project consortium. The research builds on state-of-the-art methods developed at the HAIML group that enable principled state estimation in hybrid state spaces as well as hybrid parameter learning combining gradient-based and gradient-free optimization techniques.

Application Documents and Procedure

Applications must be submitted in complete form and include all required formal documents, in particular:

  • a curriculum vitae,
  • certificates and transcripts of academic degrees,
  • proof of language proficiency.

Applicants must further submit a cover letter in which they explicitly address and document compliance with eligibility criteria (1)–(5) ("this makes you a good fit").

Incomplete applications or applications not meeting the eligibility criteria may not be considered.

THESE ARE YOUR TASKS:

  • develop and extend hybrid neuro-symbolic and neuro-probabilistic models for state estimation and activity recognition in dynamic systems with continuous and discrete state components
  • investigate hybrid learning strategies combining gradient-based and gradient-free methods for parameter learning in structured probabilistic models
  • design and implement algorithms for sensor-based activity and situation recognition, including scenario-specific model parametrization
  • contribute to the integration, evaluation, and iterative refinement of the developed methods within an end-to-end, situation-adaptive assistance system
  • conduct experimental studies using simulated and real-world sensor data in both clinical and home-care settings
  • disseminate research results through peer-reviewed conference and journal publications, presentations at international venues, and contributions to open-source

THIS MAKES YOU A GOOD FIT:

  • academic qualifications:
    • completed bachelor's and master’s degree in Computer Science, or a diploma in Computer Science or a comparative course of study with predominantly computer science curriculum
    • the qualifying degree must have been completed with a grade 2.0 or better (German grading system)
    • degree must have been awarded by a higher education institution (recognized as H+ according to the German Anabin system)
  • language proficiency: applicants must meet either of the following language profiles:
    • proficiency in written and spoken English at level C1 (CEFR) e.g. IELTS overall score ≥ 7.0, or TOEFL ≥ 94, or
    • native-level proficiency in German and demonstrated English language skill
  • research experience: documented experience in data analysis, probabilistic modeling, and/or artificial intelligence is required
  • organizational and time-management skills: demonstrated ability to work independently and reliably under time constraints and to meet deadlines
  • communication skills: proven ability to communicate scientific results effectively, both orally and in written form

WE AS AN EMPLOYER:

Equal opportunities are important to us. We welcome applications from suitable severely disabled people or people from traditionally underrepresented groups. We aim to increase the proportion of women in research and teaching and therefore encourage suitably qualified women to apply. We welcome applications from people of other nationalities or with a migration background.

WE OFFER YOU:

FURTHER INFORMATION:

We will determine the experience level individually, taking into account your previous professional experience.

If you would like to work part-time in this position, this is possible subject to the requirements of the position.

The temporal limitation of the employment relationship is based on § 2 (2) Wissenschaftszeitvertragsgesetz.

We look forward to receiving your application (cover letter, CV, degree certificate stating your final grade) by 18.02.2026 at the latest. We can only consider applications received via our homepage. Please send us your documents via the ‘Online application’ button at the end of a job offer. Unfortunately, we cannot accept e-mail applications.

Incomplete application documents may not be considered in the further course of the selection process.

Unfortunately, we can not cover application and travel costs.

Become part of the team

The Hybrid Methods in Artificial Intelligence and Machine Learning group is part of the Institute for Visual & Analytic Computing at the Faculty of Computer Science and Electrical Engineering at the University of Rostock. Its research focuses on the integration of symbolic, probabilistic, and neural approaches in AI & ML. The application domains include AI-supported digital twins, intelligent environments, situation-aware assistance, and multimodal diagnostics. The methodological focus is on sequential state estimation using hybrid Bayesian and neuro-symbolic models, as well as the integration of deep learning with symbolic background knowledge.

We look forward to receiving your application!