Master's thesis in the field of bioinformatics - Deep learning for spatial transcriptomics

The Fraunhofer-Gesellschaft (www.fraunhofer.com) currently operates 76 institutes and research units throughout Germany and is a leading applied research organization. Around 32 000 employees work with an annual research budget of 3.4 billion euros.

The Fraunhofer Institute for Cell Therapy and Immunology IZI researches and develops special problem solutions at the interfaces of medicine, life sciences and engineering.

At Fraunhofer IZI, the Bioinformatics Unit is looking for a Master’s student to work on a thesis project at the Leipzig site. The group uses computer-aided methods combined with omic-wide and multi-modal analytical procedures to identify and validate new biomarkers and new therapeutic targets.

The latest technological developments in molecular diagnostics make it possible to characterize (sub)cellular expression patterns in tissue with spatial resolution. These so-called spatial transcriptomics workflows are used in preclinical drug development, evolutionary biology and patient-oriented diagnostics. To optimize the experimental workflow, a machine learning model will be developed that supports the determination of workflow parameters.

What you will do

The topic of the master thesis is the training and validation of a neural network for the computer-aided prediction of essential parameters for the experimental design of spatial transcriptomics workflows. Using publicly available paired data sets on cell morphology and spatially resolved expression in tissue, a neural network will be trained to predict the most important workflow parameters. The generalization of the trained neural network will be tested on independent test data. In addition to the public data sets, proprietary data is also available at the Fraunhofer IZI for processing the task.

What you bring to the table

You have successfully completed a Bachelor's degree in bioinformatics, medical informatics, data science, computer science or a related subject and are currently studying for a Master's degree in one of these fields.
During your studies, you have already acquired knowledge in the areas of machine learning, statistical learning and neural networks as well as experience with ML/DL frameworks and related software (e.g. (Py)Torch, TensorFlow or SciKitlearn).
You are comfortable working with Linux command lines (CLI), bash/shell scripts and the version control system Git.
You have already had first contact with an HPC and a job scheduling system.
Basic knowledge of molecular biology is an advantage.
You like to think outside the box of your own discipline and enjoy interdisciplinary teamwork. A positive error culture and fair interaction at eye level are important to you.

What you can expect

Thanks to our close links with industry, creative research freedom meets real added value for society. Through your work, you will help shape the medicine of tomorrow and gain exciting insights into the leading organization for application-oriented research in Europe.
You will gain valuable preparation for future research or professional positions through practice-oriented work. During your time with us, we offer you personal support at eye level.
A Master's thesis in a challenging international and interdisciplinary work environment with access to advanced technologies for bioinformatic analyses, which offers you a high degree of creative freedom.

We value and promote the diversity of our employees' skills and therefore welcome all applications - regardless of age, gender, nationality, ethnic and social origin, religion, ideology, disability, sexual orientation and identity. Severely disabled persons are given preference in the event of equal suitability.

Interested? Apply online via our career portal now. We look forward to getting to know you!

We will be happy to answer any questions you may have about this position:

Nicole Schulz

Tel: +49 341 355363322

Fraunhofer Institute for Cell Therapy and Immunology IZI

www.izi.fraunhofer.de

Requisition Number: 76313 Application Deadline:

Job Segment: Biology, Computer Science, Molecular Biology, Testing, Science, Technology, Research

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