德国蒂宾根大学博士后职位---人工智能与数据科学

德国蒂宾根大学博士后职位---人工智能与数据科学

艾伯哈特-卡尔斯-图宾根大学（拉丁文：Universitas Eberhardina Carolina；德文：Eberhard-Karls-Universitaet Tuebingen）简称图宾根大学，坐落于原符腾堡伯国故都、今巴登-符腾堡州大学城图宾根，由艾伯哈特伯爵于公元1477年创建，是欧洲最古老的大学之一，德国精英大学、德国U15大学联盟、欧洲研究型大学协会、欧洲公民大学联盟、欧洲大学协会、昴宿星大学联盟成员。

Postdoctoral Research Fellow – AI & Data Science

University of Tuebingen

The AI & Data Science Fellowship Program, a cooperation between the University of Tübingen, one of thirteen German universities distinguished as excellent under the German government's initiative, and Boehringer Ingelheim, one of the leading pharmaceutical companies, is currently looking for a

Postdoctoral Research Fellow – AI & Data Science (f/m/d; E13 TV-L, 100%)

to work on cutting-edge and exciting AI & data science research topics that generate real added value for human and animal healthcare.

The initial fixed-term contract will start as soon as possible and have a duration of 2 years with possible extension.

About the project

The position is available within the “Multi-modal deep learning for biomarker discovery in mass spectrometry imaging data” project. In this research project, we aim at developing and applying machine learning methods, to associate mass spectrometry imaging data with clinical phenotypes to discover novel small molecule tissue biomarkers with characteristic spatial tissue distribution.

As a Postdoc, you will be hosted in the research group led by Prof. Manfred Claassen and collaborate with Dr. Vladimir Lekic, Central Data Science, and Dr. Michael Becker, Drug Metabolism and Pharmacokinetics, of Boehringer Ingelheim.

Your profile

Minimum requirements:

  PhD in Computer Vision, Machine Learning, or a closely related field.

  A proven track record in machine learning using deep learning techniques, including designing new architectures, hands-on experimentation, analysis, and visualization.

  Strong programming skills in Python and C++, coupled with knowledge and experience in computer vision and machine learning frameworks, such as OpenCV, TensorFlow or PyTorch, and CUDA.

  A passion for applying ML research to real world problems.

Preferred qualifications:

  research experience relevant for the position, a proven track record of publications, or contributions to machine learning codebases

  scientific knowledge of biology, chemistry, or physics

  experience working with biological or chemical data and biological or chemistry software

  experience working with real-world datasets

  experience in any of the following: large scale deep learning, generative models, deep learning for drug discovery, graph neural networks

Our offer

We are offering you an exciting research position at a highly renowned university, in a welcoming, interdisciplinary and agile team which is well- connected across institutes internationally. Prof. Claassen is committed to ensuring you the possibility to conduct research with a high amount of autonomy, a collegial work atmosphere and ongoing career mentoring.

We provide remuneration in accordance with the TV-L (collective agreement for public employees of the German federal states) as well as all corresponding benefits, e.g., extensive visa and onboarding assistance, 30 days/year of paid vacation, flexible working hours, discounted public transportation, etc.

We value diversity in science, and particularly look forward to receiving applications from women, non-binary people, and researchers from underrepresented groups across cultures, genders, ethnicities, and lifestyles. We actively promote the compatibility of science, work, studies, family life and care work. In case of equal qualification and experience, physically challenged applicants are given preference.

How to apply

Please send your application (including motivation letter, curriculum vitae, certificates, representative publications, academic references, and links to publicly available code examples (e.g., GitHub, OSF, etc.) as a single PDF to Prof. Dr. Manfred Claassen: manfred.claassenspam prevention@med.uni- tuebingen.de.

Application deadline: February 28th, 2023.

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