PhD Student in Deep Learning for Rare Variant Genetics

TN Germany; Heidelberg; Vor Ort; EUR 35.000 - 50.000; Vollzeit

Zusammenfassung

A leading research institution in Heidelberg is seeking a PhD Student in Deep Learning for Rare Variant Genetics. The role involves developing machine learning models to study the genetics of human disease, contributing to groundbreaking research in cancer prevention. Candidates should possess a relevant master's degree and experience in machine learning techniques.

Leistungen

30 days of vacation per year

Doctoral salary with social benefits

Access to international research networks

Family-friendly working environment

Sustainable travel to work: subsidized Germany job ticket

Targeted training and mentoring through the DKFZ International PhD Program

Qualifikationen

• Master's degree or equivalent in relevant fields.
• Experience in machine learning, deep learning, and Bayesian modeling.
• Effective communication skills in English.

Aufgaben

• Building machine and deep learning models for genetics research.
• Applying advanced techniques from deep learning and Bayesian modeling.
• Collaborating with multiple research groups on the TARGET-AI project.

Kenntnisse

Machine Learning

Statistics

Mathematics

Deep Learning

Bayesian Modeling

Ausbildung

Master's degree in Computer Science, Statistics, Mathematics, Physics, Engineering, or Biological Science

Jobbeschreibung

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PhD Student in Deep Learning for Rare Variant Genetics, Heidelberg

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DKFZ

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Other

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Yes

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Job Reference:

39f5a7af47f7

Job Views:

1

Posted:

20.05.2025

Expiry Date:

04.07.2025

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Job Description:

PhD Student in Deep Learning for Rare Variant Genetics

Reference number: 2025-0147

• Full-time
• Computational Genomics and Systems Genetics

“Research for a life without cancer" is our mission at the German Cancer Research Center. We investigate how cancer develops, identify cancer risk factors and look for new cancer prevention strategies. We develop new methods with which tumors can be diagnosed more precisely and cancer patients can be treated more successfully. Every contribution counts – whether in research, administration or infrastructure. This is what makes our daily work so meaningful and exciting.

The Division of Computational Genomics and Systems Genetics is seeking from October 2025 a

You will be affiliated with the laboratory of Dr. Clarke, which is part of the Division of Computational Genomics and Systems Genetics at DKFZ (https://steglelab.org). The project is executed in collaboration with the main partner of the TARGET-AI project at Helmholtz Munich, as well as supporting groups at the University of California, Berkeley and the German Human Genome-Phenome Archive. The position will also be connected to a vibrant local ecosystem for data science and machine learning.

Your Tasks

The research group of Dr. Brian Clarke is looking for a highly motivated doctoral student to join an ambitious project aimed at building machine and deep learning models to study the genetics of human disease. Funded as part of the Helmholtz AI program, the project TARGET-AI will bring together expertise from multiple research groups to advance the state-of-the-art in combining the most advanced techniques from deep learning/AI with rigorous statistical analyses, an area in which our group has a track record of success (see recent publications below).

The TARGET-AI project seeks to apply leading-edge techniques from deep learning and Bayesian modeling to gain insights into the genetic underpinnings of disease and improve genetic risk prediction. We seek to build on previous expertise and methods devised by our teams (see below), including incorporating architectures and principles from Bayesian neural networks and biological sequence models, including large DNA and protein language models. The project also aims to develop a prototype federated learning framework to enable meta-analysis of multiple large biobank datasets, all of this with the aim of increasing our sensitivity to unravel the complex genetic causes of disease and, in so doing, identify new therapeutic targets.

Clarke, B., Holtkamp, E. et al. Integration of variant annotations using deep set networks boosts rare variant association testing. Nature Genetics 56, 2271–2280 (2024).

Nappi, A. et al. Bayesian Aggregation of Multiple Annotations Enhances Rare Variant Association Testing. bioRxiv (2025).

Your Profile

The successful applicant should hold a master's degree or equivalent qualification in computer science, statistics, mathematics, physics, and/or engineering, or a degree in biological science with demonstrated experience in quantitative analysis.

We are looking for a range of talents and expertise, including in machine learning, mathematics, or statistics. Experience with one of the focus areas of the project, including deep learning, Bayesian modeling, and federated learning, is welcome.

Effective oral and written communication skills in English are required .

Our group values creativity, a rigorous scientific mindset, kindness, and positive team spirit. We encourage candidates from diverse professional and personal backgrounds to apply.

We Offer

Excellent framework conditions: state-of-the-art equipment and opportunities for international networking at the highest level

Access to international research networks

Doctoral salary with the usual social benefits

30 days of vacation per year

Possibility of part-time work

Family-friendly working environment

Sustainable travel to work: subsidized Germany job ticket

Unleash your full potential: targeted training and mentoring through the DKFZ International PhD Program and DKFZ Career Service

Our Corporate Health Management Program offers a holistic approach to your well-being