PhD Training Program on Decoding the Genetic Puzzle of Complex Traits

Job description

In recent years, the field of Artificial Intelligence (AI) has seen transformative advancements, particularly in deep learning technologies such as many-layered convolutional neural networks (CNNs). These networks excel at detecting complex patterns within high-dimensional data and integrating diverse data sources through multiple input channels. Such capabilities are highly promising in the realm of human genetics, where they can significantly enhance the analysis of omics data. Building on our previous development of the GenNet deep learning framework for omics analysis, this PhD project aims to tackle the prevailing limitations in AI applications for genetic analysis.

One major challenge in genetic research is the restricted sharing of data across studies, which diminishes analytical power compared to pooled data analyses. Federated learning, a pioneering distributed machine learning method, enables powerful data analysis without direct data access, preserving privacy and data integrity. This project aims to develop and implement a federated learning system in collaboration with NVIDIA and OASYS NOW, focusing on enhancing gene discovery power across multiple global datasets, including the Rotterdam Study, UK Biobank, and the CHARGE Consortium and EADB Consortium.

Additionally, in partnership with the Clinical Genetics Department, we will enrich the annotation of the non-coding genome, aiming to unravel the complexities of these regions that play crucial roles in gene regulation. This interdisciplinary approach not only boosts the discovery and understanding of genetic traits but also enhances the transparency and interpretability of AI models, thanks to the integration of explainable AI principles.

This project, therefore, bridges AI, genomics, and clinical genetics, employing state-of-the-art technologies to transform our approach to complex trait analysis.

Work environment

Erasmus MC is dedicated to a healthy population and excellence in healthcare. By conducting groundbreaking work, we aim to push through current boundaries and leading the way forward in research, education and healthcare. We have access to the latest equipment and techniques in a state-of-the-art environment. Our departments are known for their forefront research and facilities.

The candidate will be appointed jointly in the department of Radiology and Nuclear Medicine and department of Epidemiology. You will be working in multidisciplinary team with experts in machine learning, deep learning, genomics and epidemiology. Research group has its own GPU cluster and access to the Amsterdam super-computer.

Qualifications and skills

We are looking for enthusiastic candidates with a strong motivation to engage in the development and application of advanced analytical methods, including artificial intelligence, to tackle this important societal and healthcare challenge. Successful candidates are expected to have a strong quantitative or computer science background, excel at critical thinking, and highly motivated to solve clinical and epidemiological problems.

• The candidate should have an MSc degree in mathematics, computer science, statistics, bioinformatics, physics, electrical engineering, or in an equivalent discipline.
• Experience with Python language.
• Experience with Linux.
• Experience with machine learning and deep learning methods.
• Knowledge or willingness to learn more about epidemiology and genetics.
• Good communication and writing skills in English are required.
• Ability to work, launch and embrace collaboration in a multidisciplinary setting.

Before you apply please check our conditions for employment.

Terms of employment

• You will receive a temporary position for 4 years.
• The gross monthly salary is € 3.017,- in the 1st year and increases to € 3.824,- in the 4th year (scale OIO).
• A salary increase of 3% from July 2025, according to the collective labor agreement (cao).
• Excellent fringe benefits, such as a 13th month that is already paid out in November and a individual travel expense package. 
• An International Office which aids you in preparing for you arrival and stay. 
• Pension insurance with ABP. We take care of approximately 2/3 of the monthly contribution. 
• Special benefits, such as an incompany physiotherapist and bicycle repairer. And there is also a gym where you can work on your fitness after work.

More information

Project leader:

Dr.Gennady Roshchupkin, department of Radiology and Nuclear Medicine, department of Epidemiology, Erasmus MC. www.roshchupkin.org | www.bigr.nl | g.roshchupkin@erasmusmc.nl 

In collaboration with:

Prof. dr. Arfan Ikram, department of Epidemiology, Erasmus MC

Dr. Stefan Barakat, department of Clinical Genetics, Erasmus MC

CHARGE consortium

EADB consortium

NVIDIA Flare team

OASYS NOW

No agencies please.