Vacancy No.
IFG - Doctoral candidate, research assistant (f/m/d) as part of the MSCA EU project “NanoRAM”, research topic: “Digitalization of Biomaterial Research via Deep Learning Approaches”
In this project you will contribute to research on the edge of materials science and information technologies. You will work on the development of a deep learning algorithm assisted platform for material research, which will allow the fast screening of biomolecular interactions and biomaterial performance.
This PhD position is part of the EU Doctoral Network NanoRAM: Emerging Nanotools for Soft Matter Characterization and Manipulation. NanoRAM brings together people and organizations from across the world to train a new generation of scientists in the development and application of newly developed manipulation and characterization nanotools in soft matter research. The PhD candidate will go on secondments to partner organizations as well as participate in consortium meetings and workshops. Further information can be found on the project website https://nanoram-dn.com/.
Job description
You will contribute to the project “Digitalization of Biomaterial Research via Deep Learning Approaches” (DC10) which involves the following tasks:
- Preparation of model biomaterial surfaces using systematic variation of surface chemistry via vapour deposition polymerization of functionalized poly-p-xylylenes
- Deep learning-based analysis of fingerprint-like drying droplet patterns by training of convolutional neural networks (CNN)
- Use the CNN to extract cellular localization patterns in polarized light microscopy images via end-to-end hierarchical feature representations
- Complementary characterization via elaborate techniques (e.g. Circular Dichroism, Time-of-Flight Secondary Ion Mass Spectrometry, Imaging Ellipsometry)
- Improve the model management to ensure reproducibility by avoiding information loss
Personal qualification
- Master or equivalent in chemistry, chemical engineering or materials science.
- Profound knowledge in biochemistry, polymer chemistry and physical chemistry
- Practical experience in software programming and/or scripting
- Excellent communication and writing skills in English Language
In addition, applicants must comply with the Doctoral Network eligibility criteria:
- Must be a doctoral candidate, i.e. not already in possession of a doctoral degree at the date of the recruitment
- Mobility rule: The candidate can be of any nationality but must not have resided or carried out their main activity (work, studies, etc.) in Germany for more than 12 months in the 3 years immediately before the recruitment date
Organizational unit
Institute of Functional Interfaces (IFG)
Starting date
01.05.2024
Contract duration
3 years
Application up to
30.04.2024
Contact person in line-management
For further information please contact Prof. Dr. Joerg Lahann, Email: joerg.lahann@kit.edu
Please apply via email adhering to the following:
- Attachments must be PDF files
- Please put “Application to NanoRAM DC10 [your name]” in the subject line
Please include
- Personal information: Full name, gender, nationality and contact details.
- Motivation letter: Maximum 2 pages highlighting your academic and research experience as well as why you are interested in the NanoRAM project DC10.
- Curriculum vitae: Maximum 3 pages.
- Academic records: Original and English translation. Certified documents with grades. If the candidate has not yet completed the required degree, the documents must show the expected graduation date.
- Recommendation letters: From university lecturer, scientist or similar, who can verify your academic work and judge your potential as a predoctoral researcher. Contact information for the referee must be included as they will be contacted during the evaluation process.
Application
Please apply online using the button below for this vacancy number .
Personnel support is provided by
Ms Ratzel
phone: +49 721 608-25544,
Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
Recognized severely disabled persons will be preferred if they are equally qualified.