Phd Student In Computational Biology

The University of Gothenburg tackles society's challenges with diverse knowledge. 56 000 students and 6 600 employees make the university a large and inspiring place to work and study. Strong research and attractive study programmes attract researchers and students from around the world. With new knowledge and new perspectives, the University contributes to a better future.

The Department of Mathematical Sciences at Chalmers University of Technology and the University of Gothenburg has about 200 employees and is the largest department of mathematics in Sweden. We conduct successful research in pure and applied mathematics and mathematical statistics in a wide range of research areas and offer an international environment with frequent exchanges with other universities around the world. The department provides a friendly, creative, and supportive atmosphere with a steady flow of international guests. The department plays a crucial role as a collaborator in various scientific disciplines and industrial projects.

The announced PhD position is in the group of Marija Cvijovic (https:// www. cvijoviclab.org/), located at the Division of Applied Mathematics and Statistics. In our division there are several research groups in the area of computational biology and bioinformatics. Gothenburg offers a vibrant computational/systems biology environment with many excellent research groups actively collaborating and organizing a number of life science seminars, summer schools and workshops.

Our department continuously strives to be an attractive employer. Equality and diversity are substantial foundations in all our activities. We work actively to be a parent-friendly organization.

The PhD student is supervised by Professor Marija Cvijovic and Associate Professor Umberto Picchini and is funded by a grant from the Swedish Research Council.

Computational Biology

The research project is performed in an international and highly collaborative multidisciplinary environment. We study the complexity of cellular ageing using experimental and computational methods. The work is highly interdisciplinary with close collaboration with a number of experimental research groups in other areas.

You will work in a highly dynamic research environment at the interface between mathematics and biology to address the central problems of cellular ageing. The work involves a tight integration of mathematical modelling and biological experiments, considering the intricate interplay of numerous subsystems in the ageing process. The primary focus is on contributing to the development of methods and tools for efficient modelling pipelines that will be exploited in developing a comprehensive whole-cell model for replicative ageing. The goal is to enhance our understanding of key lifespan regulators and identify pivotal events leading to cellular senescence. The successful candidate is expected to independently develop new ideas and foster collaborations with experimentalists. This project builds on our previous results (for reference, please see https: // doi.org/10.1371/journal.pcbi.1010261 and https: // doi.org/10.1371/journal.pcbi.1010082), providing the essential infrastructure and groundwork.

As part of the PhD education, you will receive training in cutting-edge computational biology methods. Coupled with the research school's applied mathematics program, this comprehensive training will prepare you for diverse opportunities in academia, as well as in the private and public sectors.

Employment is a fixed-term position limited to a maximum of five years, four years of studies, and up to one year of departmental work, which is distributed during the whole employment period.

The Swedish Higher Education Ordinance provides eligibility criteria for academic positions. To qualify for the position, the applicant must have obtained a Master's degree or a four-year Bachelor's degree in mathematics, applied mathematics or mathematical statistics, or expect to complete such a degree by December 31, 2024.

Candidate should have excellent communication skills, strong self-organizing skills and the capacity to work effectively in an interdisciplinary environment. Fluency in English is expected.

Additionally, candidate should demonstrate a solid, documented knowledge in Bayesian statistics as well as knowledge of one or more programming languages, such as Python or Julia

Regulations for the evaluation of qualifications for education on a doctoral level are given in SFS 1998:80.

Type of employment: Fixed-term employment Extent: 100 % Location: Department of Mathematical Sciences, Gothenburg First day of employment: negotiable

Marija Cvijovic, Professor, Applied Mathematics and Statistics, [email protected]

Irina Pettersson, Associate Professor, Head of Unit of Applied Mathematics and Statistics, [email protected]

Union representatives at the University of Gothenburg can be found here: https: // www. gu.se/om-universitetet/jobba-hos-oss/hjalp-for-sokande

Submit the following documents:

Applications must be received by: 2024-12-17

Choosing a career in a foreign country is a big step. Thus, to give you a general idea of what we and Gothenburg have to offer in terms of benefits and life in general for you and your family/spouse/partner please visit:

https: // www. gu.se/en/about-the-university/welcome-services https: // www. movetogothenburg.com/

The University works actively to achieve a working environment with equal conditions, and values the qualities that diversity brings to its operations.

Salaries are set individually at the University.

In accordance with the National Archives of Sweden's regulations, the University must archive application documents for two years after the appointment is filled. If you request that your documents are returned, they will be returned to you once the two years have passed. Otherwise, they will be destroyed.

In connection to this recruitment, we have already decided which recruitment channels we should use. We therefore decline further contact with vendors, recruitment and staffing companies.