Postdoctoral Fellow in Cancer Genomics

European Commission; Deutschland; Vor Ort; EUR 50.000 - 65.000; Vollzeit

Zusammenfassung

L'Institut for Computational Cancer Biology à Cologne recherche un postdoctorant avec des compétences en apprentissage machine et AI, pour travailler sur des projets de recherche innovants en oncologie. Le candidat idéal a un PhD et une forte motivation à collaborer au sein d'équipes interdisciplinaires.

Qualifikationen

• PhD et Masters en Physique, Mathématiques, Informatique, Bioinformatique requis.
• Compétences en apprentissage machine et AI nécessaires.
• Prior experience en génomique du cancer est un plus.

Aufgaben

• Développer des algorithmes et modèles pour étudier l'évolution du cancer et l'instabilité chromosomique.
• Diriger des projets nationaux et internationaux sur l'AI et l'apprentissage machine.
• Collaborer avec des bioinformaticiens et cliniciens au CHU de Cologne.

Kenntnisse

Machine Learning

AI

Bioinformatics

Communication

Teamwork

Ausbildung

PhD in Physics, Mathematics, Computer Science, Bioinformatics or related field

Masters degree

Jobbeschreibung

Institute for Computational Cancer Biology, Cologne

Organisation/Company Institute for Computational Cancer Biology, Cologne Research Field Computer science Researcher Profile Recognised Researcher (R2) Country Germany Application Deadline 20 Jul 2025 - 23:00 (UTC) Type of Contract To be defined Job Status Full-time Hours Per Week To be defined Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

The Schwarz Lab is actively seeking a postdoctoral fellow with a background in a quantitative discipline and an interest in the development of AI and machine learning methods for cancer genomics.
About us

Our computational group is interested in understanding the role of chromosomal instability, somatic copy number alterations (SCNA) and chromatin structure in cancer evolution. We develop and apply algorithms for inferring and simulating cancer evolution and for haplotype reconstruction and phasing in close collaboration with our clinical and experimental partners. We are part of several national and international consortia, such as the ICGC-ARGO , TRACERx , and SATURN3 consortia. Our group is also part of the Berlin Institute for the Foundations of Learning and Data (BIFOLD) at the TU Berlin, with potential for exchanges and group visits.

About the project

As a successful candidate you will lead national and international projects on AI and machine learning in cancer research. You will develop algorithms and models to investigate cancer evolution and chromosomal instability. A focus lies on the development of AI methods for improved phylogenetic reconstructions and feature space embeddings from complex genomics data. You will be embedded in a thriving interdisciplinary and collaborative team of bioinformaticians and clinicians at the University Hospital of Cologne.

About you

Ideal candidates will have a PhD and Masters degree in Physics, Mathematics, Computer Science, Bioinformatics or a related field. A strong background in theoretical and/or applied machine learning and AI, and a strong interest in medical research is expected. Prior experience in cancer genomics is a plus. Successful candidates will show a strong work ethic and should be enthusiastic about working in a highly interdisciplinary environment. Excellent communication skills and the ability to work in a team are required.

For further reading and our recent works see:

We offer a competitive salary according to German E13 TVL 100%. Contracts are initially limited to 2 years with the possibility of prolongation. Applications should be submitted online here . For inquiries, please email [e-mail] . The positions are to be filled as soon as possible; applications will be considered initially until July 20 .

Number of offers available 1 Company/Institute Institute for Computational Cancer Biology, Cologne Country Germany Geofield