Research Assistant for Machine Learning/AI (m/f/d)

The Technical University of Applied Sciences Würzburg-Schweinfurt (THWS) is one of the largest practice-oriented universities in Bavaria and covers a wide range of subjects with the fields of technology, social and economic sciences, design and languages. We accompany around 9,300 students in more than 70 courses on their educational path. Since 2022, THWS has been certified by the audit family-friendly university in order to meet the diversity of life plans and family forms and to actively promote the reconciliation of the profession and family of their employees. The technology transfer centre Kitzingen (TTZ-KT) of THWS deals with the areas of robotics, artificial intelligence and digitalization. We are currently working with 25 regional partner companies, from SMEs to world companies, in different industries such as production, logistics, software development and construction companies. Our international team is led by two experienced research professors and currently employs five scientific staff. Currently we have the following place to occupy: Kitzingen Technology Transfer Centre Research Associate for Machine Learning/AI (w/m/d) in full time Application code: 24.7.776 The place shall be occupied at the next possible time, the place of service shall be located in Kitzingen. The employment is first terminated until 31.12.2027 and in full-time (40 hours/week). An extension is sought; the place is in principle part-time, provided that the full implementation of the tasks is ensured by job sharing. Do you want to bring your knowledge, skills and experience into exciting research and development projects in the field of AI and machine learning? You like to work practice-oriented and have fun working with companies? Then you are right for this place!

The following exciting tasks await you: Implementation of applied research in the field of artificial intelligence, often also in collaboration with industrial partners Developing solutions, including using modern methods of machine learning and classic artificial intelligence Writing of research applications Presentation of the technology transfer centre in public relations Publication of results in scientific journals and presentation of results at national and international conferences and workshops Supporting thesis and project work of Bachelor and Master students For Master graduates we offer the possibility of doctoral studies in our doctoral center ''Sustainable and Intelligent Systems (NISys)'' .

successfully completed academic studies (e.g. master or university diploma) or doctoral studies, preferably in the fields of artificial intelligence, robotics, computer science, applied mathematics or a related degree course, or a comparable relevant qualification Experience in one or more of the following research areas:

• large language models (LLM) /VLMs
• Representation learning (including diffusion models)
• Neuronial networks
• Statistical learning
• Reinforcement Learning
• Planning and Opimization
• Application of AI for time series analysis or predictive maintenance Programming skills, especially in the field of robotics and KI/ML (Python, C++, Julia) Experience with modern deep learning frameworks such as Tensorflow/PyTorch Professional experience from industrial activities and robotics skills (ROS) are advantageous strong communication skills and social skills good knowledge of German in word and writing at least at level B1

varied and demanding activities with flexible working time design through a sliding time account and a generous home office control 30 days of year the attractive benefits of the public service collegial cooperation in a highly motivated team with flat hierarchies and an open communication culture a wide range of possibilities for further education free parking space Benefits in the menses of the Studierendenwerk Würzburg an attractive university city with high quality of life health-promoting measures such as discounted sports courses, health days and workshops The employment and remuneration are governed by the relevant provisions of the collective agreement of the countries with all benefits customary in public service. The grouping is carried out in the case of all conditions in remuneration group 13 TV-L. Disabled applicants are preferably hired in the case of otherwise essentially the same suitability, skills and professional performance. Our university welcomes the fact that women feel particularly addressed by the call for proposals. .