Postdoc position in Machine Learning for Time Series Data Analysis (965461)

Aarhus University, Department of Engineering
Finlandsgade 22, 8200 Aarhus N, Denmark
depends on seniority as agreed between the Danish Ministry of Finance and AC Denmark
March 02 2018
Position Type
Full Time
Organization Type
Job Type

The position is a 2-year position funded by Danske Commodities A/S in the form of a Postdoc Fellowship and focuses on research and development of state-of-the-art Machine Learning and Data Analytics methodologies targeting time series data. The selected candidate will be employed by the Department of Engineering, Aarhus University and will have offices in both Aarhus University and Danske Commodities.

The position is available from June 1st 2018 or as soon as possible hereafter.

Job description
The Postdoc Fellow will be affiliated with the Signal Processing group of the Department of Engineering and will be supervised by Prof. (Docent) Henrik Karstoft and Assist. Prof. Alexandros Iosifidis. The Signal Processing group currently consists of 4 senior permanent scientific staff members, three Postdocs, and eight PhD students, of which twelve are working in the fields of Machine Learning and Signal Processing. The Signal Processing group will contribute with an extensive research environment within Machine Learning and Signal processing in general.

At Danske Commodities, the Postdoc Fellow will collaborate closely with the Automated Trading team. The Automated trading team is committed to developing and automating Danske Commodities’ trading strategies. In addition, it focuses on automating Danske Commodities’ trade support, analysis and weather forecasting tools. The team is formed by five analysts and three meteorologists. Members of the team work closely together with Danske Commodities’ trading units towards tasks prioritization and delivery on targets.

Your profile
Applicants must hold:

  • a PhD degree in Computer Engineering, Computer Science, or similar, with a strong publication track record in fields relevant to the position;
  • a solid background in Machine Learning, with focus on Deep Learning and/or Statistical Machine Learning methodologies;
  • experience in programming using Python or C++ or Matlab and related scientific libraries/ toolboxes, like TensorFlow, Caffe, Theano, MatConvNet, etc;
  • the ability to work with experts from a broad range of scientific and technology backgrounds;
  • the ability to closely collaborate with commercial/industrial partners;
  • strong communication and collaboration skills.

Who we are/ The department
Aarhus University is a leading research university, which is in the top-100 on several major global university rankings. The Department of Engineering has been recently established and is growing very rapidly in terms of students, staff and research output, thereby offering qualified and good opportunities for pioneering efforts.

Danske Commodities is the leading independent energy trading company, specializing in short-term power and gas trading. Every day, the company completes more than 3000 trades across 35 countries in Europe, constantly moving energy from where there is more than needed to where it is needed most. With more than ten years of experience, Danske Commodities utilizes its expertise, unmatched market presence and 24/7 trading setup to offer balancing, optimization and hedging services to energy producers and suppliers.

Place of Work and area of Employment     
The formal place of employment is Aarhus University, and the place of work is Science and Technology, Finlandsgade 22, 8200 Aarhus N, Denmark. The working station at Danske Commodities is at the company headquarters, Vaerkmestergade 3, 8000 Aarhus C.

Contact information
For further information please contact:

Application procedure
Shortlisting is used. This means that after the deadline for applications – and with the assistance from the assessment committee chairman, and the appointment committee if necessary, – the head of department selects the candidates to be evaluated. All applicants will be notified whether or not their applications have been sent to an expert assessment committee for evaluation. The selected applicants will be informed about the composition of the committee, and each applicant is given the opportunity to comment on the part of the assessment that concerns him/her self. Once the recruitment process is completed a final letter of rejection is sent to the deselected applicants, including the main considerations emphasized during the selection process.

Formalities and salary range
Science and Technology refers to the Ministerial Order on the Appointment of Academic Staff at Danish Universities under the Danish Ministry of Science, Technology and Innovation.

The application must be in English and include a curriculum vitae, degree certificate, a complete list of publications, a statement of future research plans and information about research activities, teaching portfolio and verified information on previous teaching experience (if any). Guidelines for applicants can be found here.

Appointment shall be in accordance with the collective labour agreement between the Danish Ministry of Finance and the Danish Confederation of Professional Associations. Further information on qualification requirements and job content may be found in the Memorandum on Job Structure for Academic Staff at Danish Universities. Salary depends on seniority as agreed between the Danish Ministry of Finance and the Confederation of Professional Associations.

All interested candidates are encouraged to apply, regardless of their personal background. Research activities will be evaluated in relation to actual research time. Thus, we encourage applicants to specify periods of leave without research activities, in order to be able to subtract these periods from the span of the scientific career during the evaluation of scientific productivity.

Aarhus University offers Relocation service to International researchers. You can read more about it here.

Read the full job description and apply online here

Application deadline: 9/4/2018

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