Phd student/posdoctoral position (f/m/d) in machine learning methods for pathomics and single cell sequencing

Uniklinik RWTH Aachen

52074 Aachen, Germany

Job posting number: #7111228 (Ref:GB-P 37226)

Posted: September 20, 2022

Job Description

As a transregional provider of secondary to quaternary care, the RWTH Aachen University Hospital combines patient-oriented medicine and nursing, teaching and research at an international level. With 35 specialist services, across 30 institutes and five interdisciplinary units, the university hospital covers the entire medical spectrum. Our unique setting of a single building hosting health care, research and teaching offers the best conditions for intensive interdisciplinary exchange and close clinical and scientific networking. Around 8,500 employees ensure that holistic patient-oriented management and nursing care is delivered consistently according to recognized quality standards over 45,000 inpatient episodes, through 1,400 beds and 200,000 outpatient attendances per year.

The Institute of Pathology and the Institute of Computational Genomics are seeking a

Phd student / posdoctoral position (f/m/d) in machine learning methods for pathomics and single cell sequencing

The position is available to start as soon as possible, it´s a full-time position (38,5 hours per week) and would be on a fixed-term contract initially for 3 years with possibility to extension.

We invite applicants for a postdoctoral / Ph.D. student position in machine learning approaches for integrative analysis of pathomics and single cell sequencing data. Our team has previously developed end-to-end anatomical features in histology slides. This allows us to characterize large cohorts of pathology slides from patients, which are already available, producing pathomics data sets with millions of anatomical structures from hundreds to thousands of patients. Single cell sequencing is also available for selected samples. The candidate will develop statistical machine learning methods to find disease related trajectories via integrative analysis of transcriptomics and pathomics data and relate these to clinical information of patients. The project will focus on kidney diseases and be embedded within the ERC consolidator grant (AIM.imaging.CKD) and the Clinical Research Unit 5011 (InteraKD – Integrating emerging methods to advance translational kidney research).

Profile:
• We are looking for highly motivated individuals with a university degree (Ph.D. or equivalent) in computer science, statistics, or bioinformatics
• Applicants should have experience in machine learning, bioinformatics and / or multivariate statistics
• Experience in high performance computing is a plus
• Willingness for teamwork and the ability to work independently in a competitive international environment are required
• Good skills of English (Level C1) are expected

Why should you choose us?
• We provide a direct education and a deep dive into real world applications in medical domain
• We provide excellent supervision and many years of experience in Al in digital pathology an computational biology
• We offer a high-end infrastructure for digital pathology including high performance GPU servers (e. g. DGX A100) and DL workstations, as well as very large, well annotated, unique an dalready availoable datasets and storage capacity
• 300 professions under one roof – more variety is not possible
• We offer performance-based remuneration in accordance with the provisions of TV-L (EG 13), including attractive public service benefits
• In addition to a wide range of further education and training opportunities
• We offer a wide range of health promotion options as well as the extensive university sports program
• Monthly employee discounts through our corporate benefits program to save on private shopping
• Flexible working hours so that you can better combine work and private life, because your work-life balance is important to us
• Attractive conditions for public transport or discounted employee parking so that you have a relaxed commute
• A company kindergarten so that your children are always close by

This position is not gender specific.

The RWTH Aachen University Hospital promotes equal opportunities and diversity. Applications from women are expressly encouraged and if the applicant is suitable qualified, they will be given priority in accordance with the LGG. If suitably qualified, people with a registered disability will also receive priority.

Weekly hours are negotiable.

Please note that taking up this role can only occur after a pre-employment health check has confirmed evidence of immunisation (or evidence of immunity) according to paragraph 20, section 3 of the infection prevention law (vaccination for COVID-19).

You should preferably use our digital application portal at www.karriere.ukaachen.de for your application. There you have the option of securing your documents in the electronic application folder to prevent unauthorized access. Applications that reach us by email to: [email protected] (this transmission path cannot be as effectively secured) will be transferred to the aforementioned portal and any accompanying documents will be disposed of in accordance with data protection regulations immediately after transfer. After the retention period has expired, the data in the portal will also be deleted. If you do not agree to a transfer to the Application portal your application cannot be considered.

Uniklinik RWTH Aachen, Institute of Pathology, Univ.-Prof. Dr.med. Peter Boor, Pauwelsstr. 30, 52074 Aachen.

Uniklinik RWTH Aachen, Institute for Computational Genomics, Univ.-Prof. Dr. Prof. Ivan G. Costa, Pauwelsstraße 19, 52074 Aachen.

The application deadline for the advertised position GB-P 37226 is 15th of November 2022.

For more detailed information please contact Univ.-Prof. Dr.med. Peter Boor or Univ.-Prof. Dr. Prof. Ivan Costa, E-Mail: [email protected] or [email protected]

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More Info

Job posting number:#7111228 (Ref:GB-P 37226)
Application Deadline:Open Until Filled
Employer Location:Freie Universität Berlin
Berlin,
Germany
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