PhD Position in Artificial Intelligence for Protein Science
PhD position in
Artificial Intelligence for Protein Science
at the
University of Bayreuth
.
The Chair of Artificial Intelligence in Protein Science invites applications for a PhD researcher to work at the interface of
machine learning
,
computational biology
,
protein science
, and
molecular biology
. The project focuses on developing and applying computational and data-driven methods to understand protein structure, function, and interactions.
The successful candidate will investigate fundamental questions in protein science using quantitative approaches, develop algorithms and machine learning/deep learning models, build and maintain software tools, pipelines, and web applications, and contribute to literature reviews, presentations, manuscripts, tutoring, and mentoring.
Applicants should hold a Master’s degree in a relevant area such as Bioinformatics, Biophysics, Computational Biology, or a related quantitative life-science discipline. Candidates from Computer Science or Machine Learning backgrounds are also encouraged to apply if they have a strong interest in molecular biosciences. Strong programming and data analysis skills, ideally in Python, familiarity with statistics and biological datasets, and very good English are expected. Experience with biological databases, omics data, or machine learning is an advantage.
The position is
65% TV-L E13
, initially for
3 years
, and is available from
July 2026
. The university highlights a supportive, interdisciplinary environment, modern research facilities, paid vacation, flexible work arrangements, conference support, and opportunities for professional development.
Application deadline:
30 June 2026.
How to apply:
Submit your application via the University of Bayreuth portal, quoting the password “Protein Science”. Include a CV, academic certificates and transcripts, a motivation letter, and two letters of recommendation with referee contact details. For questions, contact Hadeer Elhabashy at [email protected].