PhD Position in Precision Medicine for Neurological Disorders in Zürich

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100%, Basel, temporary

The Machine Learning and Computational Biology Laboratory at the Department of Biosystems Science and Engineering (D-BSSE) is seeking a PhD Student in the field of Machine Learning for Health to start on January 1st, 2020. The student will be joining a highly interdisciplinary research team and will be supervised by Dr. Catherine Jutzeler and Prof. Karsten Borgwardt.

The Department of Biosystems Science and Engineering (D-BSSE) is one of ETH Zurich's youngest departments and the only one located in Basel outside of the Zurich campus. It unites biologists, engineers, computer scientists, and mathematicians to work towards a quantitative understanding and purposeful engineering of complex biological systems.

Job description

The PhD student will be funded by Dr. Catherine Jutzeler’s recently awarded Swiss National Science Foundation Ambizione Grant on precision medicine for neurological disorders. The focus of this project will be on traumatic brain and spinal cord injury, with the potential to expand to other neurological conditions. Both disorders, traumatic brain and spinal cord injury, are highly heterogenous in terms of clinical presentation, disease progression, and associated comorbidities. This heterogeneity makes reliable prediction of future outcomes and patient stratification for clinical trials extremely challenging. The aims of this project are to (1) identify biomarkers that allow a better characterization of the patients (e.g., identification of homogenous subgroups, phenotypes, responders and non-responders); and (2) expedite the search for disease-modifying treatments through drug repurposing. To implicitly account for the multifaceted nature of the disorders, state-of-the-art machine learning methods will be applied to analyze multidimensional medical data (clinical, imaging, genetic, behavioral, and physiological), derived from completed clinical trials, health records, and observational studies. Machine learning topics relevant to the project include mining and learning on time series and graphs, kernel methods, graph kernels, multiple testing correction in ultra-high-dimensional spaces, feature selection in structured spaces, deep learning, and causal inference. 

Your profile

Applicants should be highly motivated and creative, hold a Master’s Degree in computer science, bioinformatics, computational biology, statistics, medicine, or related fields. Moreover, candidates must be able to fluently communicate in English (oral and written) and be willing to work in a highly interactive, interdisciplinary, and international environment at the interface of machine learning and biomedicine. Academic excellence, a professional work attitude, and a proactive and self-driven work ethic are expected. Advanced computer skills, including use of statistical software such as R, Python, or C++ are an asset. The position is fully funded for 4 years, and it will be located in the Machine Learning and Computational Biology Laboratory at the Department of Biosystems Science and Engineering at the ETH Campus in Basel. Ideally, the position should start on January 1, 2020.

ETH Zurich
ETH Zurich is one of the world’s leading universities specialising in science and technology. We are renowned for our excellent education, cutting-edge fundamental research and direct transfer of new knowledge into society. Over 30,000 people from more than 120 countries find our university to be a place that promotes independent thinking and an environment that inspires excellence. Located in the heart of Europe, yet forging connections all over the world, we work together to develop solutions for the global challenges of today and tomorrow.

We look forward to receiving your online application including the following documents: (1) your CV, and (2) a letter of motivation (up to 2 pages long), in which you describe your motivation to join the lab and in which you explain which publications of the lab you are most interested in. We exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered. 

Further information about the Machine Learning and Computational Biology lab can be found on our website . For questions about the position, please contact Dr. Catherine Jutzeler (no applications).

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