{"id":3994,"date":"2017-11-19T21:06:15","date_gmt":"2017-11-19T20:06:15","guid":{"rendered":"http:\/\/eubias.org\/NEUBIAS\/?p=3994"},"modified":"2018-08-29T16:52:30","modified_gmt":"2018-08-29T15:52:30","slug":"postdoc-deep-learning-medical-image-analysis","status":"publish","type":"post","link":"https:\/\/eubias.org\/NEUBIAS\/postdoc-deep-learning-medical-image-analysis\/","title":{"rendered":"Postdoc in deep learning for medical image analysis"},"content":{"rendered":"

Postdoc in deep learning for medical image analysis<\/h3>\n

KTH Royal Institute of Technology, School of Computer Science and Communication,\u00a0Stockholm,\u00a0Sweden<\/p>\n

KTH Royal Institute of Technology in Stockholm has grown to become one of Europe\u2019s leading technical and engineering universities, as well as a key centre of intellectual talent and innovation. We are Sweden\u2019s largest technical research and learning institution and home to students, researchers and faculty from around the world. Our research and education covers a wide area including natural sciences and all branches of engineering, as well as in architecture, industrial management, urban planning, history and philosophy.<\/p>\n

For information about the School of Computer Science and Communication, please visit https:\/\/www.kth.se\/en\/csc<\/a>.<\/div>\n
\n

Department information<\/strong><\/p>\n

The position will be formally placed with the department for Computational Science and Technology (CST) at KTH, but work will be carried out at the Science for Life Laboratory. The CST department conducts research to understand and model physical and biological systems using computational techniques, which require efficient, high performance algorithms and implementations together with advanced visual analysis capabilities. For more information go to https:\/\/www.kth.se\/en\/csc\/forskning\/cst<\/a>.<\/p>\n

The Science for Life Laboratory (SciLifeLab) is a collaboration between four universities in Stockholm and Uppsala: Karolinska Institutet, KTH,\u00a0Stockholm University and Uppsala University. It combines advanced technology with broad knowledge in translational medicine and molecular life sciences. Since 2013, SciLifeLab has a mission from the Swedish government to run infrastructure to support researchers nationally and to be an internationally leading center for large-scale analyses in molecular life sciences targeting research in health and environment. For more information, visit https:\/\/www.scilifelab.se\/<\/a>.<\/p>\n

Job description<\/strong><\/p>\n

This position is part of a collaboration with physicians from the Karolinska University Hospital. The main task will be to develop deep learning methods to analyse medical images, focusing on breast cancer. The successful applicant will apply his\/her knowledge in deep learning to several types of medical images, including histological sections, mammograms, and possibly others. Generally, the goal will be towards predicting patient outcome, but we aim to develop models for specific predictors of patient outcome, such as tumour heterogeneity biomarkers and risk models. In additional to these medical applications, the successful candidate will also participate in theoretical research in deep learning and computer vision. Other duties include helping to mentor MSc and PhD students, and potential teaching duties.<\/p>\n

The position is initially funded for one year, with a possibility for extension contingent upon funding and eligibility.<\/p>\n

Qualifications<\/strong><\/p>\n

Candidates must have a PhD in computer science, computational science, or a related field received within the last three years. Proven knowledge and ability in one or more deep learning frameworks (Tensorflow, Keras, Torch, Caffe, etc) is absolutely required. Also required is knowledge of standard computer vision techniques and experience in implementing, analysing, and optimizing scientific applications for image analysis. Proficiency in one or two scientific computing languages (Python, Matlab, R) is required. Experience with parallel programming environments and cloud computing is a plus. Previous experience working with medical or biological images is also desirable.<\/p>\n

Trade union representatives<\/strong><\/p>\n

You will find contact information to trade union representatives at KTH:s webbpage<\/a>.<\/p>\n

Application<\/strong><\/p>\n

Log into KTH’s recruitment system in order to apply to this position. You are the main responsible to ensure that your application is complete according to the ad.<\/p>\n

Your complete application must be received at KTH no later than the\u00a0last day of application, midnight CET\/CEST (Central European Time\/Central European Summer Time).<\/p>\n

Applications shall include the following documents:<\/p>\n

    \n
  1. Statement of interest including a brief description of experience in deep learning<\/li>\n
  2. Curriculum vitae<\/li>\n
  3. Transcripts from university<\/li>\n
  4. Reference contact information<\/li>\n
  5. Representative publications (or other example of scientific writing)<\/li>\n<\/ol>\n

    Please observe that all material needs to be in English.<\/p>\n

    \n

    Others<\/strong><\/p>\n

    We firmly decline all contact with staffing and recruitment agencies and job ad salespersons.<\/p>\n

    Disclaimer: In case of discrepancy between the Swedish original and the English translation of the job announcement, the Swedish version takes precedence.<\/p>\n<\/div>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
    Type of employment<\/th>\nTemporary position longer than 6 months<\/td>\n<\/tr>\n
    Contract type<\/th>\nFull time<\/td>\n<\/tr>\n
    First day of employment<\/th>\nAccording to the agreement<\/td>\n<\/tr>\n
    Salary<\/th>\nMonthly salary<\/td>\n<\/tr>\n
    Number of positions<\/th>\n1<\/td>\n<\/tr>\n
    Working hours<\/th>\n100%<\/td>\n<\/tr>\n
    City<\/th>\nSolna<\/td>\n<\/tr>\n
    County<\/th>\nStockholms l\u00e4n<\/td>\n<\/tr>\n
    Country<\/th>\nSweden<\/td>\n<\/tr>\n
    Reference number<\/th>\nD-2017-0814<\/td>\n<\/tr>\n
    Contact<\/th>\n\n
      \n
    • Kevin Smith \/ Bitr universitetslektor, , ksmith@kth.se, +46 8 790 64 37<\/li>\n
    • Maria Engman \/ HR-administrat\u00f6r, maengm@kth.se<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
    Published<\/th>\n14.Nov.2017<\/td>\n<\/tr>\n
    Last application date<\/th>\n06.Jan.2018 11:59 PM CET<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

    Postdoc in deep learning for medical image analysis KTH Royal Institute of Technology, School of Computer Science and Communication,\u00a0Stockholm,\u00a0Sweden KTH Royal Institute of Technology in Stockholm has grown to become one of Europe\u2019s leading technical and engineering universities, as well as a key centre of intellectual talent and innovation. We are Sweden\u2019s largest technical research and learning institution and home to students, researchers and faculty from around the world. Our research and education covers a […]<\/p>\n","protected":false},"author":11,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[10,9],"tags":[11],"yoast_head":"\nPostdoc in deep learning for medical image analysis - NEUBIAS: Network of BioImage Analysts<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/eubias.org\/NEUBIAS\/postdoc-deep-learning-medical-image-analysis\/\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Paula Sampaio\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"4 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/eubias.org\/NEUBIAS\/postdoc-deep-learning-medical-image-analysis\/\",\"url\":\"https:\/\/eubias.org\/NEUBIAS\/postdoc-deep-learning-medical-image-analysis\/\",\"name\":\"Postdoc in deep learning for medical image analysis - 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