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The Center for Advanced Systems Understanding (CASUS) is a German-Polish research center for data-intensive digital systems research. CASUS was founded in 2019 in Görlitz and conducts digital interdisciplinary systems research in various fields such as earth systems research, systems biology and materials research.
CASUS invites applications as PhD Student (f/m/d) Machine-learning-driven Digital Twin of Virus Infected Cell Dynamics.
The position will be available from 1 October 2022. The employment contract is limited to three years.
The Scope of Your Job:
The global SARS-CoV2 Pandemic has demonstrated the urgency to develop new tools for our arsenal against pathogens. If YOU are passionate about designing the next generation of mathematical models and machine learning algorithms in virology – we are looking for YOUR talent!
Viruses require host cells to procreate and spread their progeny. For this, they enter cells, replicate and egress in a stepwise process occurring through interactions between molecules of the host cell and the pathogen molecules. These are known as host-pathogen interactions, they include various host mechanisms exploitations by the pathogen. Synchronized with virus entry, uncoating, and replication through a virus genetic program, virus induce dramatic changes of cell morphology. These may be observed in cell culture using light microscopy techniques at a moderate magnification. However, connecting the dynamics of host-pathogens interactions at cellular and tissue levels requires advanced simulations.
Using Physic-informed Neural Networks in differential equation discovery mode, as well as data-driven discriminative and generative approaches developed in our group, YOU will develop a Digital Twin model of infected cell dynamics on the scale of a tissue, which will help us understand how viruses spread across the tissue.
- Engage with our international collaborators (Dresden, London, Zurich, Taipei etc.) to understand existing approaches and datasets
- Develop novel mathematical models and data-efficient algorithms for continuum modelling of host-pathogen interactions
- Design novel physics-informed deep learning architectures suitable for time-lapse microscopy data
- Publish high-impact peer-reviewed research
- Master’s degree in Computer Science, Data Science, Machine Learning, or Physics, Bioinformatics, Computational Biology, or relevant discipline
- A solid background in Physics, Mathematics, Software Engineering, Machine Learning, or in a related subject
- Excellent programming skills in languages such as Matlab and Python
- Familiarity with modern deep learning frameworks like Numpy, Tensorflow 2.x.x, PyTorch
- Previous experience or interest in Matlab to Python codebase conversion, Computer Vision, Deep Learning and Slurm cluster environments is a plus
- Strong motivation to work in a collaborative environment
- Excellent communication skills in English in a professional context (presentation of research results at scientific meetings, colloquial discussions, writing of manuscripts)
- A vibrant research community in an open, diverse and international work environment
- Scientific excellence and extensive professional networking opportunities
- A structured PhD program with a comprehensive range of continuing education and networking opportunities - more information about the PhD program at the HZDR can be found here
- The employment contract is limited to three years
- Salary and social benefits in accordance with the collective agreement for the public sector (TVöD-Bund) including 30 days of paid holiday leave, company pension scheme (VBL)
- Numerous company health management offerings
- An employer subsidy for the VVO job ticket
Kindly submit your completed application (including cover letter, CV, diplomas/transcripts, etc.) only via our Online-application-system.
For any questions, do not hesitate to ask:
Dr. Artur Yakimovich
Dr. Michael Bussmann Tel.: +49 3581 37523 11
Place of work: Görlitz
Working hours: 19,5 h/week
Deadline: 27 October 2022
The HZDR is committed to equal opportunity employment and we strongly encourage applications from qualified female candidates. We also carefully consider all applications from job candidates with severe disabilities.
Bautzner Landstraße 400