Deep Learning Engineer for Omics Data

Universität zu Köln
Köln, Germany
9 days ago

Role details

Contract type
Temporary contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English, German
Job source

Tech stack

Artificial Intelligence Data Analysis Bioinformatics Data Cleansing Python (Programming Language) Machine Learning Tensorflow Pytorch Deep Learning Information Technology Feature Extraction

Requirements

» PhD in Computer Science, Bioinformatics, Artificial

Intelligence or a related field or equivalent experience level

» Solid understanding of machine learning fundamentals, including common algorithms, model training and evaluation techniques

» Experience with Python programming and at least one AI/ML framework(e.g. Tensor Flow, PyTorch)

» Exposure to data science concepts such as data preprocessing, feature extraction, and exploratory analysis

» Exposure to biomedical data science » Curiosity and willingness to explore emerging AI domains

in the biomedical domain » Very good interpersonal and communication skills; in

particular, the ability to effectively work in a diverse, collaborative and interdisciplinary research environment

» Fluency in English - written and oral (German is not required)

Benefits & conditions

» Opportunity to receive training in cutting-edge

methods using deep learning on genomics data and their integration

» A diverse working environment with equal opportunities » Support in balancing work and family life » Flexible working time models » Extensive advanced training opportunities » Occupational health management offers

The University of Cologne promotes equal opportunities and diversity. Women will be considered preferentially in accor- dance with the Equal Opportunities Act of North Rhine- Westphalia (Landesgleichstellungsgesetz - LGG NRW). We also expressly welcome applications from all suitable candidates regardless of their gender, nationality, ethnic and social origin, religion, disability, age, sexual orientation and identity.

The position is available at the earliest possible time on a full-time basis (39,83 hours per week). The position is to be filled for a fixed term until 30 September 2028 with the possibility of an extension. If the applicant meets the rele- vant wage requirements and has the appropriate personal qualifications, the salary is based on remuneration group 13 TV-L of the pay scale for the German public sector.

About the company

We are one of the largest and oldest universities in Europe and one of the most important employers in our region. Our broad range of subjects, the dynamic development of our main research areas and our central location in Cologne make us attractive for students and researchers from around the world. We offer a wide range of career opportunities in science, technology, and administration.

The Poetsch group is looking for a Deep Learning Engineer (f/m/x) to support the team in the study of genomes and how they change with ageing and in cancer development. This is a core-funded position with a strong collaborative focus and the goal to build up deep learning infrastructures on Omics data for the lab and beyond.

Apply for this position

This job is hosted externally. Click below to view the full posting and apply.

Apply on de.indeed.com

Good distractions

Talks and stories from around this role — technically off-topic, practically not.

2:35 min

Preventing remote code execution in PyTorch models

Balázs Kiss · World Congress 2023

1:39 min

Fundamentals of tensors and the TensorFlow library

Håkan Silfvernagel · LIVE

1:48 min

Automating exploratory data analysis within training pipelines

Dora Petrella · World Congress 2023

3:14 min

Structuring career paths and localized data architectures

Ulrich Wurstbauer +1 · LIVE

1:06 min

Compiling PyTorch environments for advanced time forecasting

Christoph Lohrmann Christoph Lohrmann +1 · World Congress 2026 Europe

1:36 min

Performing exploratory data analysis to uncover underlying patterns

Julian Joseph · LIVE

Videos

See all

Related articles

See all