Amyotrophic lateral sclerosis (ALS) is a disease that causes the death of motor neuron cells, and as a result the muscles decrease in size. For the patient this implies a gradual decrease in muscle strength, including lung muscles and the heart, eventually leading to death. It is thus important to learn to understand ALS to improve drug development. Based on twin studies, ALS is known to be caused by genetic factors in the majority of cases. However, the genetic characteristics that are responsible for developing ALS have stubbornly resisted their detection. This explains why ALS currently suffers from extremely poor prognosis, with death usually occurring 2-5 years after diagnosis.

In this project, for the first time we would like to employ artificial intelligence (AI) based approaches to understand the genetic foundations of ALS. AI and in particular Deep Learning (DL) have experienced a major boost in the last 6-7 years. In the meantime, DL outperforms the human mind on e.g. image recognition and games such as Go. The goal of this project is to develop AI/DL based methods that – unlike humans – are able to ‘understand’ ALS.

There is substantial promise that this is possible. The project will be carried out in collaboration with Project MinE, a massive international effort to collect genetic data from ALS patients and healthy people as a control group. In a preliminary study we have developed a deep neural network that uses this data to predict the occurrence of ALS at promising accuracy.

This project is about developing these preliminary ideas, and develop AI/DL based tools that operate at accuracy rates sufficiently high for clinical applications. This project will help people to disentangle the genetic causes of ALS. Thereby, we will pave the way to the development of new treatment protocols, medication or lifestyle recommendations, for a disease with prognosis rates that are among the worst.

This project will be carried out together with the research group of prof. dr. Alexander Schoenhuth from the University of Bielefeld and will be supported by prof. dr. Jan Veldink from UMC Utrecht, who leads Project MinE. The project is funded by Stichting ALS.

Supervised by: Alexander Schoenhuth (promotor) and Marleen Balvert (daily supervisor).

Besides conducting the research described above, the successful candidate for this project is expected to contribute to the courses taught by the department of Econometrics and OR as a teaching assistant.

We are looking for a talented and very enthusiastic student who has:

• a (Research) Master’s degree in Mathematics, Computer Science or a related field;
• genuine interest in deep learning and genetic disease;
• programming experience is a plus;
• experience with bioinformatics is a plus;
• an excellent command of English both written and spoken;
• ambition to perform top-level research and the ability to work autonomously and within a team, and interest in interacting and delivering within a group in an interdisciplinary, international environment.

Fixed-term contract: Fixed-term, 1,0 FTE for 4 years.

Employment terms and conditions
The selected candidate will be ranked in the Dutch university employment system (UFO). The starting gross salary is € 2.325,-  per month (for a full-time appointment)  in the first year, up € 2.972,- in the final year. There is a holiday allowance (8% in May), and end-of-year bonus (8.3% in December). All university employees are covered by the so-called civil servants pension fund (ABP). Researchers from outside the Netherlands may qualify for a tax-free allowance equal to 30% of their taxable salary. The university will apply for such an allowance on their behalf. The university offers very good fringe benefits (it is one of the best non-profit employers in the Netherlands), such as an options model for terms and conditions of employment and excellent reimbursement of moving expenses.

Tilburg University

Tilburg University operates in the area of the humanities and social sciences. More than 900 scientists work at one of our five Schools, focusing on economics, business and entrepreneurship, social and behavioral sciences, law and public administration, the humanities and digital sciences, and theology. More than 600 employees in the seven Divisions of University Services support the Schools.

Department of Econometrics & Operations Research

The department offers an internationally oriented and lively research environment aiming for top quality with approximately 45 staff members and 35 PhD students. Most researchers have a strong quantitative background in mathematics and statistics and have reached international reputation in key areas of business and economics like operations research, quantitative finance, actuarial science, econometrics, mathematical economics and game theory. Many researchers have a strong focus on issues with societal impact as well, for example via the Network for Studies on Pensions, Aging and Retirement (Netspar) and via sponsored chairs.