Universität zu KölnListing closed

Deep Learning Engineer for Omics Data (f/m/x)

Köln, NW, DEIndividual contributorFound Jul 30
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pythontensorflowpytorchmachine learningdeep learningdata science

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.

YOUR TASKS

» Working closely with other lab members to convert

theoretical concepts into practical code

» Providing technical guidance to junior members and

interns

» Implementing and evaluating state-of-the-art machine

learning and deep learning techniques and algorithms,

with a strong focus on omics data

» Devising and testing new algorithms, often moving from

academic papers to working code

» Creating internal tools to speed up research, such as

automated evaluation frameworks, data annotation tools,

or specialized libraries

YOUR PROFILE

» 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)

WE OFFER

» 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.

Please apply online with proof of the required qualifications

and a motivation letter (without a photo) under

https://jobportal.uni-koeln.de

The reference number is Wiss2607-16. The application

deadline is 27 August 2026.

For further inquiries, please contact Professor Dr

Anna Poetsch (apoetsch@uni-koeln.de) and take a look at

our FAQs.

Deep Learning Engineer for Omics Data (f/m/x)

Faculty of Mathematics and Natural Sciences

Institute for Genetics (IfG) and the Cluster of Excellence for Ageing Research (CECAD)

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Universität zu KölnDeep Learning Engineer for Omics Data (f/m/x)
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