Two Postdoc Positions in Deep Learning and Large-Scale Distributed Data Science at University of Copenhagen

Data science has become one of the key drivers in various application domains that are faced with huge amounts of data. This is the case, for instance, in remote sensing and astronomy, where current projects already produce petabytes of data every year. Upcoming ones will produce such data volumes every month, day, or even hour. Storing and processing such amounts of data as well as retrieving insights from the data can become very challenging and is subject of ongoing research. We are searching for up to two postdoctoral researchers in this field. The main focus of the first position is on applying state-of-the-art deep learning techniques for challenging applications and on developing conceptually new deep learning schemes. The second position aims at developing new distributed data science techniques that can take advantage of modern cluster computing frameworks such as Apache Spark.
Data science techniques have also gained a considerable attention in industry. A significant part each project will be related to extending existing and to establishing new collaborations with industrial partners.
Further information about DIKU and the Machine Learning group are linked at http://www.diku.dk/english/ and http://www.diku.dk/english/research/imagesection/m..., respectively. You can read more about IDAS at http://big-data.dk.
The positions are open from 1 January 2018 or as soon as possible thereafter. The positions are for two years.
The university wishes our staff to reflect the diversity of society and thus welcomes applications from all qualified candidates regardless of personal background.
Terms of employment
The position is covered by the Memorandum on Job Structure for Academic Staff.
Terms of appointment and payment accord to the agreement between the Ministry of Finance and The Danish Confederation of Professional Associations on Academics in the State.
Depending on seniority, the gross basic salary range for Postdoctoral Fellows is DKK 32,745 to DKK 34,476 per month (level 6 to 8) (about EUR 4,100 to 4,400 per month). A supplement could be negotiated, dependent on the candidate’s experience and qualifications. An additional 17.1 % monthly contribution to pension will be paid by the employer.
The application, in English, must be submitted electronically by clicking APPLY NOW below.
Qualifications
The candidates are expected to have a profound background in data science and machine learning with previous work conducted in at least one of the fields mentioned above. Good programming skills, preferably in Python, are also required.
Please include
Curriculum vita
Diplomas (Master and PhD degree or equivalent)
Complete publication list
Separate reprints of 3 particularly relevant papers
The deadline for applications is 15 November 2017, 23:59 GMT +1.
After the expiry of the deadline for applications, the authorized recruitment manager selects applicants for assessment on the advice of the Appointments Committee. All applicants are then immediately notified whether their application has been passed for assessment by an expert assessment committee. Selected applicants are notified of the composition of the committee and each applicant has the opportunity to comment on the part of the assessment that relates to the applicant him/herself.
You can read about the recruitment process at http://employment.ku.dk/faculty/recruitment-proces....
Further Information
Assistant Professor, Fabian Gieseke, e-mail: fabian.gieseke-AT-di.ku.dk
APPLY NOW
Part of the International Alliance of Research Universities (IARU), and among Europe’s top-ranking universities, the University of Copenhagen promotes research and teaching of the highest international standard. Rich in tradition and modern in outlook, the University gives students and staff the opportunity to cultivate their talent in an ambitious and informal environment. An effective organisation – with good working conditions and a collaborative work culture – creates the ideal framework for a successful academic career.