Two PhD Research Fellowships in statistics and machine learning available at the Department of Mathematics of the University of Oslo, Norway. The fellowship is for a period of 3 years, with starting date to be agreed upon.
Applicants must have a Master of Science degree in statistics, data science, machine learning, mathematics or a related quantitative subject, with proven competence in statistics. Candidates without a Master’s degree have time until 31.08.2019 to complete their final exam.
The ideal candidates have some experience in methodological statistics, and an outstanding potential and genuine interest to develop statistical methodology to solve important applied problems. Excellent programming capacity is required.
The PhD positions are part of the centre of excellent BigInsight, where we develops statistical and machine learning methodologies, analytical and computational tools to extract knowledge from complex and big data. Rather than being merely data driven, we investigate approaches that exploit statistical models describing dynamics, mechanisms and structures of the underlying processes.
The PhD candidate will be employed by the University of Oslo, thus enjoying all legal benefits in such a position, including health insurance, pension contributions and family welfare benefits. The working language is English.
Salary ca 50.000 Euro per year plus pension, insurance and more welfare privileges.
All details on the call can be found here, as well as how to apply:
Deadline: 23. April 2019
For further information regarding the positions, please contact: Professor Ingrid K. Glad, firstname.lastname@example.org
We would like to bring your attention to the upcoming workshop:
TOWARDS IN SILICO-GUIDED CLINICAL TRIALS IN CANCER
to be held in Oslo, 15-16 May 2019 at Scandic Holmenkollen Hotel.
We bring together experts in systems medicine, mathematical oncology and bioinformatics to discuss novel concepts for personalise cancer medicine. Check the workshop website for more details: https://osloinsilico2019.weebly.com/
Robert A. Gatenby, Moffitt Cancer Center, USA
Ivo Gut, Centre for Genomic Regulation, Spain
Francesca Buffa, University of Oxford, UK
Gyan Bhanot, Rutgers University, USA
Peter Van Loo, The Francis Crick Institute, UK
Sampsa Hautaniemi, University of Helsinki, Finland
Wenyi Wang, MD Anderson Cancer Center, USA
Haralampos Hatzikirou, Helmholtz Center, Germany
Dominique Barbolosi, Aix Marseille University, France
Rebecka Jörnsten, University of Gothenburg, Sweden
Mark Robertson-Tessi, Moffit Cancer Center, USA
Julia Casado, University of Helsinki, Finland
Kevin Leder, University of Minnesota, USA
Shridar Ganesan Rutgers Cancer Institute, USA
Peter A. Fasching Erlangen University Hospital, Germany
Jasmine Foo University of Minnesota, USA
Alvaro Köhn-Luque, University of Oslo, Norway
Registration is free but mandatory in a first come first serve bases for up to 75 participants. It includes two full days of lectures, lunches and coffee breaks with refreshments (thanks to funding from BigInsight, UiO: Life Science, NORBIS, Norwegian Biochemical Society and Digital Life Norway).
We hope many of you will join. If so, you should register as soon as possible.
The workshop organisers:
Vessela N. Kristensen, Institute of Clinical Medicine, University of Oslo
Alvaro Köhn-Luque, Oslo Centre for Biostatistics and Epidemiology, University of Oslo
Arnoldo Frigessi, Oslo Centre for Biostatistics and Epidemiology, University of Oslo
Jasmine Foo, University of Minnesota, USA
A PhD position in Statistics, Risk and Reliability Analysis is available at the Department of Mathematics, University of Oslo.
Deadline for submitting applications is 7th April. More details about the position and information on how to apply can be found at
If you know any good candidates, then it would be great if you would forward this message to them.
Please do not hesitate to contact me if you have any question.
Sven Ove Samuelsen
Forskere har klart å forutse hvordan kreften til pasienter utvikler seg. Nå håper de at dataprogrammet skal kunne gi skreddersydd behandling til fremtidens kreftsyke.
Staphylococcus epidermidis is an ubiquitous colonizer of healthy human skin, but it is also a notorious source of serious nosocomial infections with indwelling devices and surgical procedures such as hip replacements. It has not been known whether all members of the S. epidermidis population colonizing the skin asymptomatically are capable of causing such infections, or if some of them have a heightened tendency to do so when they enter either the bloodstream or a deep tissue.
Prof Jukka Corander from OCBE UiO, and part of BigInsight's focus on infectious diseases, joined a team of microbiologists and geneticists to unravel this mystery. By combining large-scale population genomics and in vitro measurements of immunologically relevant features of these bacteria, they were able to use machine learning to successfully predict the risk of developing a serious, and possibly life-threatening infection from the genomic features of a bacterial isolate.
This opens the door for future technology where high-risk genotypes are identified proactively when a person is to undergo a surgical procedure, which has high potential to reduce the burden of nosocomial infections caused by S. epidermidis.