PhD/Postdoc Position in Bioinformatics: Heidelberg University, Germany, offers an exciting and interdisciplinary PhD or Postdoc position at the Center for Molecular Biology (ZMBH). The project focuses on applying deep learning models to understand the evolution of mammalian gene regulation. Candidates will gain hands-on research experience in computational biology, artificial intelligence, and evolutionary genomics, working in a collaborative and internationally recognized research environment.
PhD/Postdoc Position in Bioinformatics – Deep Learning Models and the Evolution of Mammalian Gene Regulation
Designation
- PhD Candidate or Postdoctoral Fellow
Research Area
Deep Learning Models and the Evolution of Mammalian Gene Regulation
Location
- Center for Molecular Biology (ZMBH), Heidelberg University, Germany
Overview of Position
| Category | Details |
|---|---|
| Designation | PhD or Postdoc |
| Department | Center for Molecular Biology (ZMBH) |
| Research Areas | Computational Biology, Bioinformatics, Machine Learning, Evolutionary Genomics |
| Workplace | Heidelberg University (in collaboration with the Kaessmann and Sasse labs) |
| Salary | As per German public sector TV-L E13 salary guidelines |
| Working Hours | Flexible hours, includes additional benefits such as a public transport subsidy and paid leave |
Eligibility/Qualification
The applicant must meet the following qualifications:
- Academic Background: Holding a Master’s (for PhD candidates) or Ph.D. degree (for Postdoc applicants) in one of the following disciplines:
- Computational Biology
- Bioinformatics
- Machine Learning
- Computer Science or similar fields
- Required Skills:
- Firm knowledge and experience in computational biology.
- Proficiency in deep learning libraries such as PyTorch or equivalent tools.
- Additional Assets:
- Familiarity with single-cell genomic data analysis and an interest in mammalian evolution and gene regulation.
Job Description
Selected candidates will work collaboratively in a dual-lab research setting:
- Kaessmann Lab:
- Focuses on analyzing single-cell genomic data to understand gene regulation evolution across mammals.
- Sasse Lab:
- Develops Deep Genomic Sequence-to-Function (S2F) models to explore how genomic sequences influence gene regulatory functions.
Key Role & Responsibilities:
- Develop and test Deep Learning-based S2F models for the analysis of regulatory sequences in genomic data.
- Work on single-cell or cell-type-specific datasets from various species to identify cis-regulatory elements involved in gene regulation.
- Conduct multi-species training and comparative genomics studies to reveal evolutionary insights about phenotypic and developmental innovations.
Additional Opportunities:
- Collaborate with renowned institutes like EMBL, DKFZ, HITS, and the Max Planck Institute for Medical Research in the Heidelberg region.
- Gain technical expertise in the overlapping domains of AI, computational genomics, and evolutionary biology.
How to Apply
Interested candidates are required to submit the following documents:
- Letter of Motivation: A short description of your interest in the position and relevant skills.
- Curriculum Vitae (CV): Include prior research experience.
- Academic Transcripts: Copies of relevant degree certificates and grades.
- References: Contact details of 2–3 referees who can provide recommendations.
Application Submission:
- Email Address: Send your application to Dr. Alexander Sasse at office-sasse@zmbh.uni-heidelberg.de.
Last Date for Apply
- Closing Date: January 10, 2025
This opportunity is ideal for scholars passionate about integrating deep learning with life sciences to address evolutionary and regulatory questions in mammalian genomics. Qualified female candidates and disabled persons are encouraged to apply under Heidelberg University’s diversity policies.
For more details on the institution, visit:
