Postdoctoral Researcher in Computational Genomics - Heidelberg, Deutschland - Springer Nature

Springer Nature

Geprüftes Unternehmen

Heidelberg, Deutschland

vor 2 Wochen

Geschrieben von:

Lena Wagner

beBee Recruiter

Beschreibung

Postdoctoral Researcher in Computational Genomics - Causal Machine Learning for Gene Regulation:

Employer
European Molecular Biology Laboratory (EMBL)
Location
Heidelberg
Salary
Competitive
Closing date
12 May 2024
Discipline

Life Science
Job Type
Postdoctoral
Employment - Hours
Full time
Duration
Fixed term
Qualification
PhD
Sector
Academia- You need to sign in or create an account to a job.

About the team/job
Our research group is pioneering computational methods for deciphering molecular variation across individuals, space, and time.

We have a track record in the development of computational methods for high-throughput omics data, machine learning for multi-omics integration and causal discovery.

Our interdisciplinary and international team is jointly located at DKFZ and EMBL Heidelberg, and is connected to the vibrant local ecosystem for data science, machine learning and computational biology in Heidelberg (including ELLIS Life Heidelberg and AI Health Innovation Cluster).

Your role
You will work in a team together with experimentalists and other computational

You have

A PhD or equivalent qualification in computer science, statistics, mathematics, physics, and/or engineering, or a degree in biological science with demonstrated experience in computational and statistical development Experience with implementing machine learning or statistical learning (e.g. Bayesian inference, deeplearning) Programming skills in Python and experience with frameworks like PyTorch, Keras, Pyro or TensorFlow The Motivation and Dedication to lead a scientific project and you are a supportive, creative and responsible team member.

You might also have

Why join us
EMBL is curiosity-driven, community-oriented and international.

As an inclusive, equal opportunity employer, we believe that diversity enables us to collaborate more effectively and be innovative in our approaches.

We are, therefore, committed to creating an inclusive and flexible culture - one where everyone can realise their full potential and make a positive contribution to our organisation.

EMBL offers attractive conditions and benefits appropriate to an international research organisation with a very collegial and family-friendly working environment.

Competitive salary and social security benefits, financial support for relocation, a relaxed culture, professional development, an on-site nursery, canteen and other staff facilities make EMBL a great place to work.

What else you need to know

We are Europe's research laboratory for the life sciences - an intergovernmental organisation performing scientific research in disciplines including molecular biology, physics, chemistry and computer science.

We are an international, innovative and interdisciplinary laboratory with more than 1900 employees from many nations, operating across six sites, in Heidelberg (HQ), Barcelona, Hinxton near Cambridge, Hamburg, Grenoble and Rome.

Our mission is to offer vital services in training scientists, students and visitors at all levels; to develop new instruments and methods in the life sciences and actively engage in technology transfer activities, and to integrate European life science research.

The working language of the institute is English.

Please note that appointments on fixed term contracts can be renewed up to 9 years in total, depending on circumstances at the time of the review.

EMBL is a signatory of DORA. Find out how we implement best practices in research assessment in our recruitment processes here.

Recent publications:

Rohbeck, M., Clarke, B., Mikulik, K., Pettet, A., Stegle, O.

, & Ueltzhöffer, K Bicycle:

Intervention-Based Causal Discovery with Cycles. Proceedings of Machine Learning Research. 3rd Conference on Causal Learning and Reasoning accepted)

Moeed, A. et al. Identifying Effects of Disease on Single-Cells with Domain-Invariant Generative Modeling. Advances in Neural Information Processing Systems Velten, Britta, et al. Identifying temporal and spatial patterns of variation from multi-modal data using MEFISTO. Nature Methods (2022), 1-8. Kleshchevnikov, Vitalii, et al. Cell2location maps fine-grained cell types in spatial transcriptomics. Nature Biotechnology (2022): 1-11. Argelaguet, R., et al. Multi-Omics factor analysis disentangles heterogeneity in blood cancer.

Molecular systems biology :
e8124.