"Machine Learning and AI in clinical diagnostics and biomedical research" at the Institute of Clinical Chemistry and Laboratory Medicine, University Hospital and Medical Faculty Carl Gustav Carus of the Dresden University of Technology.
The Institute of Clinical Chemistry and Laboratory Medicine is an institute of the University Hospital Carl Gustav Carus and the Faculty of Medicine of the Technical University of Dresden.
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You will work in a multidisciplinary, international environment of researchers, scientists and physicians to develop innovative, computational methods to research molecular disease mechanisms so that patients can quickly benefit from high-tech medicine. To this end, you will work in the CIOBio research group of Dr. Josch Konstantin Pauling. Here we research and develop highly innovative computational methods for precise patient stratification and molecular subtyping for improved and individually optimized health care.
* Contribution to the development and implementation of machine learning and artificial intelligence methods in clinical diagnostics and biomedical research
* Application of classical statistical methods for the processing and analysis of raw data from various omics datasets
* Development of innovative AI and machine learning approaches for the integration and analysis of large, multimodal datasets
* Publication of scientific results in internationally recognized journals and presentation at scientific conferences
* Participation in teaching activities such as lectures and seminars in the field of clinical chemistry and laboratory medicine
* Successfully completed university degree, preferably in the fields of bioformatics, computational biology, biochemistry, biotechnology, biomedicine or comparable qualification
* Very good knowledge of software engineering (web services), as well as scientific data analysis of biological data / biological data science
* Very good programming skills in the programming languages Python and PyTorch, as well as R. Knowledge of JAVA is an advantage.
* Good knowledge of machine learning, large language models and containers
* Basic knowledge of the measurement methodology of various OMICS technologies, in particular sequencing technologies and mass spectrometry including the respective raw data analysis
* General proficiency in molecular biology/medicine
* Theoretical know-how, as well as basic knowledge in the planning and implementation of scientific projects, project coordination and project management
* Very good English language skills written and spoken
* General scientific knowledge of respective research topics and communication with scientists with various different scientific backgrounds
The part-time position (26 hours per week) is available at the earliest possible date, and is limited to three years. Salary is based on the provisions of the applicable labour agreement. xwminpx Depending on the personal requirements, classification in pay grade E13is possible.
* Activity in leading medical research combined with a highly innovative working environment
* Discussion and implementation of own ideas and strategies in an interdisciplinary top team
* Salary: According to TV-L (pay grade E13, subject to personal qualifications), including annual special payment
* Vacation: 30 days of paid annual leave
* Professional development: Extensive internal and external training opportunities to continuously enhance your skills
* Corporate culture: A transparent, team-oriented environment with shared successes and joint events, including participation in sporting events such as the REWE Team Challenge
* Mobility: Job bike option and subsidy for public transport ticket
* Health: Access to modern sports and fitness programs at our health center, mental health support programs, and comprehensive occupational health management
* Shopping benefits: Employee discounts at the hospital pharmacy, exclusive corporate benefits, and additional shopping platforms
* Work-life balance: Family office support for childcare, holiday programs, and caregiving responsibilities, as well as flexible working models