[CODATA - Data Science Training] Phd Candidate in Machine Learning

Felix Emeka Anyiam felixemekaanyiam at gmail.com
Fri Feb 1 02:26:50 EST 2019


*About the position*

Are you passionate about engaging in doctoral level research to solve
scientific problems within the area of Reinforcement Learning and Optimal
Control, a topic with great industrial significance? If yes then this
position may be for you. This exciting research project will be carried out
at the Department of Engineering Cybernetic (https://www.ntnu.edu/itk), in
close collaboration with the NTNU Artificial Intelligence initiative, and
the Dept. of Computer Science. The research will be performed under the
supervision of Prof. S. Gros and his colleagues.

*Job description*

Machine Learning (ML) is increasingly used in control applications. E.g.
the early successes in Autonomous Driving are partially due to substantial
progresses in ML. A number of other applications, mostly in robotics and
autonomous systems are benefitting from these developments. Within ML,
Reinforcement Learning (RL) is a specific class of techniques that deals
with the optimal control of dynamic systems. RL has attracted a lot of
academic and public attention lately by beating masters in Chess and Go
games. Despite its early successes, the current state-of-the-art in RL
suffers from some drawbacks, making it difficult to introduce in industrial
applications. In this PhD project, we will investigate and develop novel
ideas combining RL, optimal control and Model Predictive Control in order
to address some of the deficiencies in the field. You will carry out
research on this topic in collaboration with Prof. S. Gros and his
colleagues. The focus will be on theory and simulations, but some
laboratory experiments are possible.

*Qualification requirements*

The PhD-position's main objective is to qualify for work in research
positions. The qualification requirement is completion of a master’s degree
or second degree (equivalent to 120 credits) with a strong academic
background in Control, Optimization and Machine Learning or equivalent
education with a grade of B or better in terms of NTNU’s grading scale
<https://innsida.ntnu.no/wiki/-/wiki/English/Grading+scale>. Applicants
with no letter grades from previous studies must have an equally good
academic foundation. Applicants who are unable to meet these criteria may
be considered only if they can document that they are particularly suitable
candidates for education leading to a PhD degree.

The appointment is to be made in accordance with the regulations in force
concerning State Employees and Civil Servants and national guidelines for
appointment as PhD, post doctor and research assistant.

*Other required qualifications:*

   - Strong background in MPC, Optimal Control, and Reinforcement Learning
   - Strong mathematical skills (systems dynamic, probability and
   statistics, optimization theory)
   - Excellent Matlab or Python programming skills
   - Excellent written and oral English skills

*Personal characteristics*

   - Strong analytical skills, synthetic, capable of handling and
   expressing complex ideas easily
   - Strong capabilities for abstract and mathematical thinking
   - Resilient and ambitious, self-motivated
   - Team player, good at collaborating, respectful
   - Resourceful, autonomous and independent

In the evaluation of which candidate is best qualified, emphasis will be
placed on education, experience and personal suitability, in terms of the
qualification requirements specified in the advertisement

*We offer*

   - exciting and stimulating tasks in a strong international academic
   environment
   - an open and inclusive work environment with dedicated colleagues
   - favourable terms in the Norwegian Public Service Pension Fund
   <https://www.spk.no/en/>
   - employee benefits

*Salary and conditions*

PhD candidates are remunerated in code 1017, and are normally remunerated
at gross from NOK 449 400 before tax per year. From the salary, 2 % is
deducted as a contribution to the Norwegian Public Service Pension Fund.

The successful candidate will be appointed for a period of 3 years, with
possible extension to a fourth year if the candidate undertakes teaching
related duties.

Appointment to a PhD position requires admission to the PhD programme in
Eng. Cybernetic (https://www.ntnu.edu/studies/phtk). As a PhD candidate,
you undertake to participate in an organized PhD programme during the
employment period. A condition of appointment is that you are in fact
qualified for admission to the PhD programme within three months.

Appointment takes place on the terms that apply to State employees at any
time, and after the appointment you must assume that there may be changes
in the area of work.

*General information*

Working at NTNU <https://www.ntnu.edu/nirs>

A good work environment is characterized by diversity. We encourage
qualified candidates to apply, regardless of their gender, functional
capacity or cultural background. Under the Freedom of Information Act
(offentleglova), information about the applicant may be made public even if
the applicant has requested not to have their name entered on the list of
applicants.

Questions about the position can be directed to Prof. S. Gros, e-mail
sebastien.gros at ntnu.no , grosse at chalmers.se .

*About the application:*

Publications and other academic works that the applicant would like to be
considered in the evaluation must accompany the application. Joint works
will be considered. If it is difficult to identify the individual
applicant's contribution to joint works, the applicant must include a brief
description of his or her contribution.

Please submit your application electronically via jobbnorge.no
<https://id.jobbnorge.no/Account/Login?ReturnUrl=%2Fauth%2Fauthorize%3Fclient_id%3Djobbnorge.jobseeker.angular%26redirect_uri%3Dhttps%253A%252F%252Fwww.jobbnorge.no%252Fjobseeker%252FSignIn%26response_type%3Dtoken>
with
your CV, diplomas and certificates. Applicants invited for interview must
include certified copies of transcripts and reference letters.

Please refer to the application number *2019/4040* when applying.

*Application deadline: 01.04.2019.*

*NTNU - knowledge for a better world*

The Norwegian University of Science and Technology (NTNU) creates knowledge
for a better world and solutions that can change everyday life.

*Department of Engineering Cybernetics (ITK)*

Engineering cybernetics is the study of automatic control and monitoring of
dynamic systems. We develop the technologies of tomorrow through close
cooperation with industry and academia, both in Norway and internationally.
The Department contributes to the digitalization, automation and
robotization of society. The Department of Engineering Cybernetics
<https://www.ntnu.edu/itk>is one of seven departments in the Faculty of
Information Technology and Electrical Engineering <https://www.ntnu.edu/ie/>
.

*Deadline* 01.04.2019
*Employer* NTNU - Norwegian University of Science and Technology
<http://www.ntnu.no/>
*Municipality* Trondheim
*Scope* Fulltime
*Duration* Project
*Place of service* Gløshaugen

-- 

Felix Emeka Anyiam

*Research Officer & Data Analyst/Scientist*

Centre for Health and Development

University of Port Harcourt (UNIPORT)

Top Floor, Medical Library Building

University of Port Harcourt Teaching Hospital (UPTH), Port Harcourt

River State, Nigeria.

 ORCID ID: http://orcid.org/0000-0003-2774-7406

 Skype ID: @felix.emeka.anyiam

tel: +234 (0) 806 499 5462


email: chd at uniport.edu.ng

http://www.uniport.edu.ng/centres/170-centre-for-health-and-development-chd.html

*The CODATA-RDA School of Research Data Science, ICTP, Trieste Italy
Alumni  <https://vimeo.com/232209813>*

 I don't mind not knowing.  It doesn't scare me.  - Richard Feynman

To consult the statistician after an experiment is finished is often merely
to ask him to conduct a post mortem examination. He can perhaps say
what the experiment died of. -Ronald A. Fisher.

This message has been scanned for malware by Avast. www.avast.com
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://lists.codata.org/pipermail/data_science_training_lists.codata.org/attachments/20190201/38f447a8/attachment-0001.html>


More information about the Data_science_training mailing list