Doctoral / Post-doctoral researcher position in mathematical modelling of ovarian cancer

Doctoral / Post-doctoral researcher position in mathematical modelling of ovarian cancer

The growing body of patient-derived data and insights into tumour evolution has given rise to an exciting field focused on developing mathematical models to understand the spatial and dynamic progression of cancer. A particularly interesting direction is the so-called digital twin, which means to mathematically model a patient or patient cohort and use the model to predict response to different treatments. The digital twin approach also addresses one of the major obstacles in collaborative research efforts, i.e., the accessibility of original patient data, which is frequently impeded by legal restrictions, intellectual property concerns, and stringent privacy regulations. These barriers not only limit collaborative efforts but also constrain the pace of discovery and innovation. By creating synthetic datasets that faithfully replicate the statistical and biological properties of the original data, digital twins provide a viable alternative that preserves data integrity while ensuring compliance with legal and ethical standards.

We are seeking an outstanding student or post-doctoral researcher to join an exciting academic-industrial collaboration between Orion and the Systems Biology of Drug Resistance in Cancer research group. This project focuses on ovarian high-grade serous carcinoma (HGSC), a devastating disease in critical need of improved treatment options. The research leverages unique, patient-derived data from the DECIDER observational clinical trial, which offers one of the most extensive datasets for HGSC, including sequencing, imaging, and clinical information. The primary goal of the project is to develop a mathematical model that facilitates:

• personalized therapeutic strategies by customizing treatments based on individual patient profiles, enhancing treatment efficacy while minimizing adverse effects.
• efficient drug development by accelerating the drug discovery process through virtual simulations and predictions.
• in-depth understanding of HGSC resistance to treatments by uncovering the complexities of HGSC disease and resistance mechanisms

A successful candidate should have:

• An MSc or PhD in applied mathematics, computational biology, statistics, bioinformatics, or related field.
• Strong programming skills in R, Python or MATLAB.
• Desire and skills to work with multi-omics and clinical data.
• Good knowledge of mathematical modelling and basic understanding of generative models and simulation techniques.
• Good scientific reporting skills.
• Good written and spoken English skills.
• Ability to effectively execute and manage scientific activities.
• Knowledge of cancer biology and/or HGSC is considered an advantage.

We offer:

• Exciting academy-industry joint research project with cutting-edge infrastructure and real-world cancer patient data.
• Cross-disciplinary and friendly working environment in the Research Program in Systems Oncology (ONCOSYS) premises.
• Competitive salary.

Indicative publications:

• Lahtinen et al. Evolutionary states and trajectories characterized by distinct pathways stratify patients with ovarian high grade serous carcinoma. Cancer Cell, 2023. [pdf]
• Häkkinen et al. PRISM: recovering cell-type-specific expression profiles from individual composite RNA-seq samples. Bioinformatics, 2021. [pdf]
• Kozlowska et al. Mathematical modeling predicts response to chemotherapy and drug combinations in ovarian cancer. Cancer Research, 2018.  [pdf]

Terms of employment
This is a full-time position based in Biomedicum 1, Helsinki (www.biomedicum.fi), Finland, and is expected to start on February 1st 2025, or earlier for an initial period of at least two years with a possibility of extension. Trial period of six months will be applied.
 
Salary will be based on the Finnish University salary system for research and teaching staff. Demand level and personal performance level salary components depends on the candidate’s qualifications and experience.

In addition, the University of Helsinki offers comprehensive services to its employees, including occupational health care and health insurance, unemployment and pension fund, a generous holiday package, sports facilities, and opportunities for professional development. The University provides support for internationally recruited employees with their transition to work and live in Finland. For more on the University of Helsinki as an employer, please see https://www.helsinki.fi/en/about-us/careers.

How to apply
To apply for the position, please send your motivation letter and a complete CV, including a list of publications and contact information for two references, through the University of Helsinki electronic recruitment system by clicking on the Apply link. Internal applicants (i.e., current employees of the University of Helsinki) please submit your applications through the Employee Login.
 
Apply before January 10th 2025. The sooner the better as the position will be filled immediately when an outstanding candidate has been found.

For further information please visit our websites at Systems Biology of Drug Resistance in Cancer, Orion and/or contact Professor Sampsa Hautaniemi (sampsa.hautaniemi@helsinki.fi).

The University of Helsinki welcomes applicants of any gender, age, linguistic or cultural background, or minority group.

About the Faculty of Medicine
The Faculty of Medicine promotes high-quality scientific research and provides research-based undergraduate and postgraduate education in medicine, dentistry, psychology, and logopedics, as well as an international Master's Programme in Translational Medicine. In addition to its teaching and research activities, the faculty serves as a significant expert organization in the healthcare sector and contributes to ethical discourse in the field.

Together with HUS Helsinki University Hospital and the Helsinki Institute of Life Science (HiLIFE), the Faculty of Medicine constitutes an academic medical centre recognized for excellence in international comparisons.

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