Medicinska fakulteten, Institutionen för kliniska vetenskaper, Lund

Lund University was founded in 1666 and is repeatedly ranked among the world’s top universities. The University has around 47 000 students and more than 8 800 staff based in Lund, Helsingborg and Malmö. We are united in our efforts to understand, explain and improve our world and the human condition.

Lund University welcomes applicants with diverse backgrounds and experiences. We regard gender equality and diversity as a strength and an asset.

The Department of Clinical Sciences Lund is one of six departments at the Faculty of Medicine. Clinical Sciences Lund cooperates closely with Skåne University Hospital and the Faculty of Medicine in order to optimize the conditions for preclinical and clinical translational research, research strategies and development, as well as education on both undergraduate and postgraduate levels.

Within the department we aim to maintain a good working environment based on mutual respect and consideration. We constantly strive to improve the conditions for work, development and participation for all employees.

Research group for breast and lung cancer
Breast cancer is the most common malignancy in women worldwide, with approximately 8000 new cases per year in Sweden. Breast cancer is a heterogeneous disease at the molecular level. This heterogeneity translates into differences in clinical manifestation, patient therapies, and ultimately patient survival. There has been an intense focus on the molecular/genomic characteristics of breast cancer over the last decades. This has revealed intriguing new insights into the biology of the disease of which some findings have also now made its way into the clinic as treatment decision support tools.

However, therapeutic progress has been different for the current clinical subtypes of breast cancer. While the majority of breast cancer patients today receive some form of targeted therapy, there are still clinical subgroups of the disease for which we currently lack effective targeted treatment. One such example is the subgroup of triple negative breast cancer (TNBC) comprising approximately 9% of all patients today in Western countries. TNBC is strongly associated with young age at diagnosis, heredity through the BRCA1 and BRCA2 genes, DNA repair deficiency, aggressive clinical course, and early relapses. Improved understanding and better markers for risk stratification and treatment is needed in TNBC to improve survival, reduce patient and family suffering, and healthcare cost.

The research group is now looking for a PhD student for research education in the project ”Image and multi-omics machine learning analysis in breast cancer”. The position is full time for four years. Preliminary starting date is October 1st 2020 or upon agreement.

Work assignments
PhD education in bioinformatics with a particular focus on breast cancer and machine learning. The PhD education involve projects aimed at breast cancer genomics with focus on image-based machine learning and pipeline development and integrative analyses of different -omics technologies (DNA, RNA, epigenetics, proteogenomics). Tasks include data analysis, method development, result summarization, project planning and scientific writing. Active participation in regional, national, and international scientific meetings and seminars is expected

The purpose of the PhD thesis is to apply and/or develop image-analysis and machine learning techniques and pipelines to a large and well characterized breast cancer cohort that can then be expanded to other subgroups or malignancies. Our goal is to this way gain a better understanding of breast cancer as a disease and thereby derive tools for improved prognostication and treatment prediction.

The PhD thesis will be performed at the Division of Oncology, Department of Clinical Sciences Lund.

Qualifications
Applicants should fulfil the basic eligibility requirements for the Faculty of Medicine (see https://www.medicine.lu.se/study/phd-programme/prospective-phd students/entry-requirements) with a degree in a relevant subject for the project.

Applicants should also have an excellent command of English, both written and spoken.

Meriting

  • A second degree (MSc or BSc) in molecular sciences or similar topic.
  • A second degree in computer science/bioinformatics.
  • Previous experience in image-based analysis using machine learning, e.g. Master thesis work.
  • Previous experience in working with breast cancer related research projects.
  • Computational skills in required programming languages, e.g. demonstrated through teaching assignments. 

Eligibility
Students with basic eligibility for third-cycle studies are those who: Have completed a second-cycle degree; have completed courses of at least 240 credits, of which at least 60 credits are from second-cycle courses, or have acquired largely equivalent knowledge in some other way, in Sweden or abroad.

The employment of doctoral students is regulated in the Swedish Code of Statutes 1998:80. Only those who are or have been admitted to PhD studies may be appointed to doctoral studentships. When an appointment to a doctoral studentship is made, the ability of the student to benefit from PhD studies shall primarily be taken into account. In addition to devoting themselves to their studies, those appointed to doctoral studentships may be required to work with educational tasks, research and administration, in accordance with specific regulations in the ordinance.

 

Type of employment
Limit of tenure, four years according to HF 5 kap 7§.

Type of employment Temporary position
First day of employment 2020-10-01 or upon agreement
Salary Monthly salary
Number of positions 1
Full-time equivalent 100
City Lund
County Skåne län
Country Sweden
Reference number PA2020/2746
Contact
  • Johan Staaf/main supervisor, 046-2221444
  • Anne Vähäniemi/HR coordinator, 046-2228758
Union representative
  • OFR/ST:Fackförbundet ST:s kansli, 046-222 93 62
  • SACO:Saco-s-rådet vid Lunds universitet, 046-222 93 64
Published 26.Aug.2020
Last application date 15.Sep.2020 11:59 PM CEST

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