|Location:||Helsinki - Finland|
|Placed On:||9th September 2020|
|Closes:||30th September 2020|
The University of Helsinki is an international scientific community of 40,000 students and researchers. It is one of the leading multidisciplinary research universities and ranks among the top 100 international universities in the world.
The Institute for Molecular Medicine Finland (FIMM) is an international research unit focusing on human genomics and personalised medicine at the Helsinki Institute of Life Science (HiLIFE) of the University of Helsinki - a leading Nordic university with a strong commitment to life science research. As part of Academic Medical Center Helsinki in Meilahti campus FIMM collaborates locally with the Faculty of Medicine, Helsinki University Hospital and National Institute for Health and Welfare. FIMM is part of the Nordic EMBL Partnership for Molecular Medicine, composed of the European Molecular Biology Laboratory (EMBL) and the centres for molecular medicine in Norway, Sweden and Denmark, and the EU-LIFE Community.
FIMM is currently seeking
Postdoctoral Researcher in machine/deep learning applied to population-scale health records and genetics
The Data Science and Genetic Epidemiology Lab (https://www.dsgelab.org/) lead by Dr. Andrea Ganna is interested in the development or application of methods to early identify common preventable diseases. In our group, we integrate electronic health records, image data and genetic information from large biobank-based studies (e.g. https://www.finngen.fi/en). Our team includes experts in machine learning, human genetics and epidemiology, and and we have recently launched the COVID19 Host Genetic Initiative (https://www.covid19hg.org/), to study the genetic determinants of COVID19 severity.
Our lab is based at FIMM and at Massachussets General Hospital/Harvard Medical School and affilitated with the Broad Institute. The candidate would be able to work periodically in both locations, if necessary. FIMM also provides excellent training opportunities for postdoctoral researchers through the FIMMPOD postdoctoral program.
Qualification and requirements: We are looking for future research leaders to work with an unprecedented amount of health information (diagnoses, medications, socio/demographic information, image data, etc..) from millions of individuals in combination with biobank-based genetic data to develop and pilot cutting edge approaches for predicting complex diseases and generating synthetic health data. The successful candidate should prove a solid understanding of generative models, differential privacy techniques, and longitudinal data analysis from a biostatistical and/or machine/deep learning (i.e. recurrent neural networks, transformer models) perspective. Understanding of epidemiological design and measures is considered a plus.
The candidate should hold a Ph.D. in the field of machine learning, statistics/mathematics, computational sciences, or genetic epidemiology, and preferably have a strong track record in developing and applying machine/deep learning approaches to health/genetic data. The ideal candidate shows scientific independence, has publishing experience, and an aptitude towards developing techniques. Together with the PI, he/she is jointly responsible for coordinating projects and supervising Ph.D. students.
Given the current COVID19 pandemic we will also consider candidates that want to work mostly remotely.