A PhD research position is available at IBM – Zurich Research Laboratory, as a part of the H2020 HELICAL consortium, focused on understanding rare autoimmune diseases.
Background: Humans are affected by approximately 100 distinct autoimmune diseases, which are caused by a breakdown in immune tolerance to self-antigens. In recent years, large amounts genomic, epigenetic, transcriptomic and clinical datasets, as well as environmental and epidemiological datasets from patients with chronic diseases have been produced and archived. Advances in information science and artificial intelligence provide unprecedented opportunities for using these datasets to elucidate the complex biology of these disorders, how it is influenced by environmental triggers, and to personalize their management. HELICAL is an Innovative Training Network (ITN) that will focus on autoimmune vasculitis, a rare autoimmune disease, and will apply informatics to such datasets to gain new biological insights and translate new biological insight into practical clinical outputs.
Clinical challenge: The key clinical challenge in caring for people with vasculitis is balancing the immune system inhibition (required to control the disease) against over-suppression (leading to risk of death and morbidity from infection or cancer). Standard therapeutic approaches are associated with severe adverse events, such cataract, osteoporosis, infection, diabetes, hypertension and accelerated cardiovascular disease. HELICAL will depart from the current state of the art and foster a precision medicine approach in vasculitis by developing tools that can identify and predict disease flares, inform clinicians about opportunities to discontinue immunosuppressive medication, and identify therapeutic strategies that target relevant components of the immune system and blood vessel wall, leaving intact the ability to fight infection and malignancy.
Data challenge: The successful candidate will develop machine learning and artificial intelligence methods to analyze data from autoimmune vasculitis patients. A computational challenge in the analysis of rare diseases is the high heterogeneity of data sources, where datasets are generated by different studies and techniques, have different data structures and frequent missing data. In addition, there is a need to develop solutions that can ingest, process in real time and present these data in a readily usable format. The student will explore data fusion methodologies in the “small n, big p” domain, and develop prototype machine learning solutions for the prediction of vasculitis flares using diverse multi-dimensional environmental, clinical and app-based data, as well as molecular data from patients.
Mobility rule: This is an MSCA-ITN fellowship and the following rule applies:
Researchers must not have resided or carried out their main activity (work, studies, etc.) in the country of the recruiting beneficiary (Switzerland) for more than 12 months in the 3 years immediately before the recruitment date. Compulsory national service, short stays such as holidays, and time spent as part of a procedure for obtaining refugee status under the Geneva Convention are not taken into account.
More information can be found in the Guide for Applicants of H2020 programs:
ec.europa.eu/research/participants/data/ref/h2020/other/guides_for_applicants/h2020-guide-appl-msca-itn_en.pdf
Diversity: IBM is committed to diversity at the workplace. With us you will find an open, multicultural environment. Excellent flexible working arrangements enable both women and men to strike the desired balance between their professional development and their personal lives.
Keywords: Machine learning, deep learning, data fusion, systems medicine.
- Strong background in physics, mathematics, computer science or similar.
- Strong background in artificial intelligence, machine learning and/or deep learning.
- Working knowledge of statistics and mathematical modeling.
- Working knowledge of Python, C or C++.
In addition, experience in systems biology or systems medicine would be helpful, but not essential.
Please, send application material (email, cover letter and reference letters) by email at: mrm at ibm dot zurich dot com