Phd Student Position
vor 3 Wochen
(We are targeting applicants of all genders)
Full time | limited | Announcement number: 745-2023
Project description
Being at the intersection between spatial biology and machine learning, the project aims at deciphering the complex information present in multiparametric tissue-wide imaging datasets using machine learning. Based on algorithms such as graph neural networks, we will develop novel computational tools to quantify how single cells interact in the 3D bone marrow space in animal models and cancer patient samples. Our goal is to leverage those findings towards designing novel therapeutic strategies to improve disease-free survival in the context of cancer.
We expect
- Master’s degree in computer science, bioinformatics, statistics, physics or equivalent
- An excellent knowledge of machine learning and statistics is essential
- Very good knowledge of python and best practices in software development is required
- Experience with graph neural networks and 3D computer vision is a plus
- Passion for discovery, collaborative spirit & good communication skills
- Good knowledge of spoken and written English
- Basic experience in laboratory techniques and background in cancer is a plus, but not required
**Offer**:
- The position (65% - E 13) is initially limited for 3 years with the possibility for extension
- **Mobility**:Free public transport in all Hessen (Free State Ticket Hessen)
- **Campus**:Our attractive university hospital campus offers a modern cafeteria, various cafes, and opportunities to rest in numerous green spaces. A walk on the riverside of the Main offers relaxation during breaks
- **Work-Life-Balance**:Part-time employment is possible, we offer childcare in our daycare center (more information can be found here), child care during holidays
- **Health Promotion**: Benefit from our attractive health offers. We offer regular online and face-to-face courses on nutrition, relaxation, sports and exercise.
- **Professional development**:Collaboration opportunities with national & international expert laboratories
- Training on cutting-edge single-cell technologies
- Participation in training and qualification programs of the MSNZ and the Goethe University
We are targeting applicants of all genders and nationalities. Applications from women are therefore particularly welcome. Disabled applicants are preferred if they have the same personal and professional qualifications.
**Relevant Publications**
Qoku A & **Buettner F.** Encoding Domain Knowledge in Multi-view Latent Variable Models: A Bayesian Approach with Structured Sparsity. _In International Conference on Artificial Intelligence and Statistics_ (AISTATS)_ _2023 (pp. 11545-11562). PMLR.
Hekler A, Brinker TJ & **Buettner F**. Test Time Augmentation Meets Post-hoc Calibration: Uncertainty Quantification under Real-World Conditions. _In Proceedings of the AAAI Conference on Artificial Intelligence_ (AAAI)_ 2023 (Vol. 37, No. 12, pp. 14856-14864).
Buggenthin F+, **Buettner F+**,, **Kokkaliaris KD**,, Schroeder T. Prospective identification of hematopoietic lineage choice by deep learning. _Nature Methods_. 2017;14(4):403-6. **+co-first authors**
**Kokkaliaris KD**, Kunz L, Cabezas-Wallscheid N, Christodoulou C, Renders S, Camargo F, Trumpp A, Scadden DT, Schroeder T*. Adult blood stem cell localization reflects the abundance of reported bone marrow niche cell types and their combinations._ _Blood_. 2020; 136(20):2296-2307. **co-corresponding authors (Cover story)**
Coutu D**+**, **Kokkaliaris KD+**, Kunz L+, Schroeder T. Multicolor quantitative confocal imaging cytometry. _Nature Methods_, 2018; 15: 39-46**. +co-first authors**
Coutu D+, **Kokkaliaris KD+,** Kunz L, Schroeder T. Three-dimensional map of nonhematopoietic bone and bone-marrow cells and molecules. _Nature Biotechnology_, 2017; 35(12):1202-1210. **+co-first authors** **(Cover story)**
Join our team
Further information on the Dr. Senckenberg Institute of Pathology can be found here.
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