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德国达姆施塔特工业大学计算机科学系招聘计算机视觉项目博士后
文章来源:知识人网整理       更新时间:2020年07月30日

德国达姆施塔特工业大学计算机科学系招聘计算机视觉项目士后

The Visual Inference Lab (led by Prof. Stefan Roth, Ph.D.) at the Department of Computer Science of TU Darmstadt is offering a position starting 01.11.2020 or later as

Research Associate (PostDoc) (f/m/d)

in the ERC-funded project “Robust, Explainable Deep Networks in Computer Vision” (RED) initially limited to three years, with the option of extension.

In this ERC project, we aim to advance significantly deep networks in computer vision toward improved robustness and explainability. To this end, we will investigate (i) structured network architectures bridging deep networks with classical approaches to computer vision, (ii) probabilistic/Bayesian deep networks in computer vision, and (iii) interpretable hybrid generative/discriminative models for scene analysis, all with the goal of increasing robustness and gaining explainability. This is accompanied by (iv) research on how to assess robustness and aspects of explainability through appropriate datasets and metrics.

The RED project will be carried out jointly by a team of up to four doctoral researchers as well as one PostDoc, and is directed by Prof. Stefan Roth, Ph.D.

The Visual Inference Lab carries out leading research in various areas of computer vision with an emphasis on using and developing machine learning/deep learning approaches and statistical methods. We develop models and algorithms, e.g., for semantic image analysis, motion estimation from videos, object recognition and tracking, as well as image restoration. Our open and internationally oriented team has access to state-of-the-art GPU facilities, including nvidia DGX machines, enabling outstanding research.

TU Darmstadt is an autonomous university with broad research excellence, an interdisciplinary profile, and a clear focus on engineering as well as information and communication technology. It is one of Europe’s leading AI hotspots (see csrankings.org) and a unit in the European Laboratory for Learning and Intelligent Systems (ELLIS, ellis.eu). The Department of Computer Science is one of the most visible departments of TU Darmstadt and regularly ranks among the best CS departments in Germany.

Your profile

  Outstanding doctorate in computer vision or machine learning

  A strong publication record in the top vision and/or learning conferences (CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR) is a prerequisite

  Very good knowledge of English

  Experience with supervision of student projects/theses

  High intrinsic motivation, reliability, creativity, as well as the ability to discuss, formulate, and present scientific results in English

  Significant potential for outstanding, independent research on challenging scientific problems as well as the ability for teamwork

The fulfillment of the duties likewise enables the scientific qualifications of the candidate. Opportunity for further qualification (habilitation) is given.

The Technische Universität Darmstadt intends to increase the number of female employees and encourages female candidates to apply. In case of equal qualifications applicants with a degree of disability of at least 50 or equal will be given preference. Wages and salaries are according to the collective agreements on salary scales, which apply to the Technische Universität Darmstadt (TV-TU Darmstadt). Part-time employment is generally possible.

Your application should contain a cover letter, curriculum vitae, as well as diplomas and transcripts (PhD, Master, Bachelor, and high school). Additionally, please include your three most important publications/papers. Your cover letter should discuss your motivation and qualifications for the position. Moreover, please name at least two references, who would be willing to supply letters of recommendation. Please send electronic applications (in the form of a single PDF), referencing the code no., to Prof. Stefan Roth, Ph.D. (email: info@visinf.tu-darmstadt.de).

Code No. 380

Published on

July 28, 2020

Application deadline

September 15, 2020


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