PhD Candidate in Computational Biology: Simulating Large T-Cell Collectives (0.8 - 1.0 FTE)
PhD Candidate in Computational Biology: Simulating Large T-Cell Collectives
Employment: 0.8 - 1.0 FTE
Maximum gross monthly salary: € 3,061
Faculty of Science
Required background: Research University Degree
Duration of the contract: 4 or 5 years
Application deadline: 28 February 2021
We are looking for
A PhD Candidate who is curious about building a large-scale simulation of the human immune system and using it to improve our knowledge about how the real immune system learns.
When you hear about 'artificial intelligence', you might think of computer programs or systems that are modelled after the brain - the biological system that is most commonly associated with the term 'intelligence'. But our bodies harbour a second intelligent system that is just as critical for our ability to thrive in this world: the adaptive immune system. And while the brain has been very much at the centre of attention in computer science in recent years, fuelled by the rise of 'artificial networks', there remain vast expanses of unexplored territory when it comes to understanding the immune system from a computational perspective. The Computational Immunology research group is looking for a PhD candidate to join the quest for building an 'artificial immune system': a computer simulation of how the T-cells that make up a large part of that system, process and store information, make decisions, learn, get confused, and forget.
Artificial immune systems have a long history dating back to the late 1980s, but for several years, only relatively small systems have been built. Like artificial neural networks in their early days, these early artificial immune systems were not very successful at reproducing biological phenomena or solving machine learning tasks. Over several years, our group has developed new algorithms that allow us to scale up such artificial immune systems by several orders of magnitude, which finally makes it possible to build simulations that resemble the real system in terms of size and complexity. You will further develop these new tools and build large-scale simulation models that can process real data on how the immune system responds to external stimuli (such as a virus infection) and make predictions that can be tested by comparing them with experimental data. Through iterations between model predictions and experiments (which are performed by our collaborators), you will harness your models to generate insight into the underlying principles of information processing and learning in the immune system. For an example of how this works, please refer to our most recent paper.
Ultimately, you will use your simulation to try and predict immune responses against specific foreign stimuli, such as a specific protein sequence (comparable to, for instance, a piece of the SARS-COV2 spike protein). If such predictions were possible, even if only in part, they would greatly improve our ability to design safe vaccines and other immunotherapeutic treatments rapidly. They would also help us understand how a patient's exposure to past pathogens might affect their responses to other pathogens in the future.
Of course, you will not work towards these goals on your own. Within our group, this position is part of a larger project, funded by a Vidi grant from the Dutch Research Council (NWO), and is part of a broader research line that also includes a new project funded by the Human Frontiers Science Program (G7 nations). The Computational Immunology group is affiliated with both Radboud University (Data Science section) and the Radboud university medical center (Tumor Immunology Department). Within our NWO and HFSP projects, we also collaborate closely with Dr Judith Mandl's group at McGill University, Montreal, with whom we regularly exchange students and staff. In summary, we provide ample opportunity for working with and learning from world-class scientists in both data science and immunology.
Since all our teaching is in English, this position does not require any knowledge of Dutch.
You have a Master's degree in a STEM field.
You like to code, and you enjoy solving complex problems by writing good code.
You are willing to work within a diverse team and to learn new things from your colleagues.
You are excited about using computational tools to gain insight into complex biological systems.
You will join the Computational Immunology group, a 6-strong team led by Johannes Textor. Our group studies one of nature's most amazing creations, the adaptive immune system, from a computational and information-centric perspective. How do cells in the immune system process information? How do they learn, forget, and get confused? How do they collaborate with each other to make better decisions collectively than they could individually? We strive to harness the insights we gain for the benefit of both immunology and computer science. We have been involved in cancer research, the development of vaccines, and research into viruses, but we have also made important contributions to building better nature-inspired machine learning algorithms. Our group is diverse in many respects and hosts computer scientists, a medical doctor and a molecular biologist under the same umbrella. We are passionate about good science and we care about doing things well and getting them right. You will be appointed at the Data Science section of the Institute for Computing and Information Sciences (ICIS). During recent evaluations, ICIS has been consistently ranked as the No. 1 Computing Science department in the Netherlands. Evaluation committees praised our flat and open organisational structure, our ability to attract external funding, our strong ties with other disciplines, and our strong contacts with government and industrial partners. The Data Science group is well known for its research in machine learning, and is part of a unit of the European Laboratory for Learning and Intelligent Systems (ELLIS).
We want to get the best out of science, others and ourselves. Why? Because this is what the world around us desperately needs. Leading research and education make an indispensable contribution to a healthy, free world with equal opportunities for all. This is what unites the more than 22,000 students and 5,000 employees at Radboud University. And this requires even more talent, collaboration and lifelong learning. You have a part to play!
Employment: 0.8 ( 5 year contract) - 1.0 FTE (4 year contract).
the gross starting salary amounts to €2,395 per month based on a 38-hour working week, and will increase to €3,061 from the fourth year onwards (salary scale P).
In addition to the salary: an 8% holiday allowance and an 8.3% end-of-year bonus.
Duration of the contract: you will be appointed for an initial period of 18 months, after which your performance will be evaluated. If the evaluation is positive, the contract will be extended by 2.5 years (4 year contract) or 3.5 years (5 year contract).
Your education task may be up to 10% of your appointment.
You will be able to use our Dual Career and Family Care Services. Our Dual Career and Family Care Officer can assist you with family-related support, help your partner or spouse prepare for the local labour market, provide customized support in their search for employment and help your family settle in Nijmegen.
Have a look at our excellent employment conditions. They include a good work-life balance (among other things because of the excellent leave arrangements), opportunities for development and a great pension scheme.
Would you like more information?
For more information about this vacancy, please contact:
Johannes Textor, Associate Professor
Please address your application to Johannes Textor and submit it, no later than 28 February 2021, 23:59 Amsterdam Time Zone.
Your application should include the following attachments:
Letter of motivation.
Something interesting you have produced, such as a program, an essay or a video, and which you are most proud of, or deem relevant to the position
The interviews will be scheduled on 11, 12, and 15 March 2021.
We drafted this vacancy to find and hire our new colleague ourselves. Recruitment agencies are kindly requested to refrain from responding.