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<p class="MsoNormal">PhD position<o:p></o:p></p>
<p class="MsoNormal">‘Transfer learning for personalised cancer treatment’<o:p></o:p></p>
<p class="MsoNormal"><o:p></o:p></p>
<p class="MsoNormal">Collaborating Institutes:<o:p></o:p></p>
<p class="MsoNormal">Technical University of Delft (TUDelft), Delft<o:p></o:p></p>
<p class="MsoNormal">VU University Medical Center, VUMC, Amsterdam and<o:p></o:p></p>
<p class="MsoNormal">The Netherlands Cancer Institute (NKI), Amsterdam.<o:p></o:p></p>
<p class="MsoNormal"> <o:p></o:p></p>
<p class="MsoNormal">Project description. How can we find better diagnostic markers for early stages of cancer? How can we better predict which therapy is best for which patient? The aim of the ‘COMPUTE CANCER’ project is to develop a novel methodological and
computational framework that improves the selection of diagnostic and predictive biomarkers from cancer genomics data. Our strategy is to adapt known prediction methods (e.g. penalized regression, random forests) to allow for the inclusion of ‘co-data’: auxiliary
information on the genomic variables derived from other data sources. Examples include p-values from related external studies or genomic and drug response data from cancer cell lines.
<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Interdisciplinary collaboration. The group of Mark van de Wiel (Biostatistics, VUMC) will focus on developing ‘weighted learners’, where the challenge is to determine weights from co-data in an automatic and objective way. The groups of
Marco Loog, Marcel Reinders and Lodewyk Wessels (TUDelft and NKI) will develop transfer learning approaches that exploit large cell line response data sets to predict therapy response in tumors. We will focus on response to chemotherapy in neo-adjuvant breast
cancer and metastatic colorectal cancer. The project is embedded within broad oncological collaborations within the VUMC as well as the NKI. The successful candidate will be employed at the TUDelft and will spend at least two days per week at the NKI. Regular
meetings between project partners and oncologists will be held to generate sufficient cohesion and momentum.<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Candidate profile. We are seeking a highly motivated PhD candidate with:<o:p></o:p></p>
<p class="MsoNormal">- A degree in bioinformatics, physics, statistics or a related discipline<o:p></o:p></p>
<p class="MsoNormal">- Experience in statistics, machine learning and/or pattern recognition
<o:p></o:p></p>
<p class="MsoNormal">- Proficiency in bioinformatics programming languages (e.g. R, Python)<o:p></o:p></p>
<p class="MsoNormal">- Good cross-disciplinary collaborative and communication skills
<o:p></o:p></p>
<p class="MsoNormal">- Experience in analysing high-throughput molecular data is a plus<o:p></o:p></p>
<p class="MsoNormal">- Experience in cancer biology and clinical applications is a plus<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Interested? Please send CV and motivation letter to Magali Michaut (m.michaut@nki.nl) & Lodewyk Wessels (l.wessels@nki.nl). Please include the names and contact information of at least two references.<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Deadline: 17 April 2017<o:p></o:p></p>
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