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, clinical data and AI-driven modelling for cancer research! In this role, you will bridge the gap between machine learning, computational biology, and haematological oncology. You do not need to arrive as an
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for education and research. Learn more about our codes of conduct We are located on one physical campus, in the heart of Amsterdam's Zuidas business district, with excellent location and accessibility. Over 6,150
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survival time of the uncured depending on covariates (risk/prognostic factors, treatments). Your work will focus on developing statistical learning approaches that allow for high dimensional covariates and a
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-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At TU/e we challenge you to take charge of your own learning process . An excellent technical
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10% teaching load and gives you the opportunity to make a start with/work on your teaching portfolio for the University Teaching Qualification. Would you like to learn more about what it’s like
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research environment A self-motivated, proactive, and self-driven working attitude Curiosity-driven self-learning ability Written and oral communication skills in Dutch Keep in mind that this describes
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strong affinity for language data; solid programming skills (e.g., Python) and experience with machine learning or NLP, ideally including transformer-based models and word embeddings; excellent English
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or the willingness to learn Dutch is considered an advantage, given the clinical component of the project. You are an enthusiastic team player who contributes to a positive and collaborative working environment
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profiles. Identification of molecular signatures in platelet disorders and bleeding disorders of unknown cause (BDUC). Development of machine learning models for patient stratification. Integration
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Requirements MSc in Physics, Materials Science, Chemistry, or related field Proficiency in spoken and written English Capacity and interest to quickly acquire new knowledge and master new skills Problem-solving