【简介】博士后招聘网整理分享“瑞士苏黎世大学2020年招聘博士后职位（生物医学领域的机器学习）”，浏览查询更多博士后招聘计划请访问博士后招聘网。

瑞士苏黎世大学2020年招聘博士后职位（生物医学领域的机器学习）

Department of Quantitative Biomedicine

Postdoc Opening in Machine Learning in Biomedicine

The University of Zurich together with the University Hospital of Zurich are embarking on a concerted effort to develop informatics programs to advance biomedical research using cutting edge computational approaches. As part of these efforts, the Krauthammer research group investigates topics in clinical data science and translational bioinformatics, such as knowledge discovery from Big Data sources (Electronic Medical Record), development of Natural Language processing, information retrieval and extraction routines, as well as the analysis of human Omics data. The group is headed by Prof. Michael Krauthammer and is part of the Department of Quantitative Biomedicine (DQBM).

Your responsibilities

For this position, we are looking for motivated PostDoc candidates who are interested in applying their computational skills to medical as well as biological problems. An example of Machine Learning (ML) in biology is our latest work on genome editing tools (base editors) for basic research and gene therapy. We developed BE-DICT 1), an attention-based deep learning algorithm capable of predicting base editing outcomes with high accuracy.

An example of ML in healthcare is our work on time series analysis for patient readmission prediction. In this work 2), we explored the systematic application of neural network models for predicting 30 days all-cause readmission after discharge from a HF hospitalization. And more recently, we are focused on the analysis of patient trajectories (i.e. using patients' medical history) and "patient similarities" (i.e. patient similarity assessment on longitudinal health data) for care pathway/knowledge discovery, and personalized outcome prediction 3). Our goal is to develop state-of-the- art approaches and build best-in-class methods to capitalize on digital clinical information to automatically compare, analyze and visualize complex longitudinal patient journeys focusing on the concept of patient journey similarity. This involves building decision support systems powered by predictive algorithms for guiding patient therapy across all disease stages, the assessment of treatment effects using counterfactual inference and the identification of causal mechanisms driving disease progression.

1) Marquart, K. F., Allam, A., et al. Predicting base editing outcomes with an attention-based deep learning algorithm trained on high-throughput target library screens. bioRxiv 2020.07.05.186544 (2020) doi:10.1101/2020.07.05.186544.

2) Allam, A., Nagy, M., Thoma, G. & Krauthammer, M. Neural networks versus Logistic regression for 30 days all-cause readmission prediction. Sci. Rep. 9, 9277 (2019).

3) Allam, A., Dittberner, M., Sintsova, A., Brodbeck, D. & Krauthammer, M. Patient Similarity Analysis with Longitudinal Health Data. (2020). https:// arxiv.org/abs/2005.06630

Your profile

PhD degree in computer science (focused on machine learning), optimization, statistics, applied math or closely related discipline.

Strong publication record with at least one first-author paper in top-tier conferences (such as NeurIPS, ICML , AISTATS, AAAI, ICLR, etc.)

Proficient in Python and the scientific computing stack (SciPy, Numpy, Scikit- learn, pandas)

Proficient in one of the deep learning frameworks (PyTorch, Tensorflow)

What we offer

Access to state-of-the-art infrastructure (computational resources), clinical datasets and medical expertise domain-knowledge (excellent medical doctors and research scientists)

Ability to make a real and tangible impact in healthcare research

Solve real-world problems and improve hospital-related process and workflow

Stimulating research environment and a place to grow academically and professionally

Outstanding working conditions at the University of Zurich.

Place of work

Zurich, Switzerland

Start of employment

Employment start date to be mutally agreed.

请您在邮件申请时在标题注明信息来自：博士后招聘网-boshihoujob.com，电话咨询时说明从博士后招聘网(www.boshihoujob.com)看到的博士后招聘信息。

声明：凡本网注明“来源：XXX”的文/图等稿件，本网转载出于传递更多信息及方便产业探讨之目的，并不意味着本站赞同其观点或证实其内容的真实性，文章内容仅供参考。如其他媒体、网站或个人从本网站转载使用，须保留本网站注明的“来源”，并自负版权等法律责任。作者如果不希望被转载或者联系转载等事宜，请与我们联系。邮箱：boshihoujob@163.com。