[Job-offers-cs] Research Associates/Fellow in modelling of electric aircraft fleet operations (3 posts, fixed-term)

[Pekka Orponen]

-------- Forwarded Message --------
Subject: [DMANET] Research Associates/Fellow in modelling of electric 
aircraft fleet operations (3 posts, fixed-term)
Date: Mon, 1 Mar 2021 16:13:28 +0000
From: Daniel Karapetyan <daniel.karapetyan at gmail.com>
To: dmanet at zpr.uni-koeln.de

A move to more electric, hybrid electric and eventually fully electric 
aircraft has the potential to greatly decrease emissions and greatly 
benefit the environment. These changes will involve operational changes, 
however, at airports and for airlines, potentially resulting in more but 
smaller aircraft replacing the larger jets that are currently in use. 
This project will build upon previous work to create a simulation which 
can be used to evaluate the effects of potential changes.


Three outstanding candidates are required to support Nottingham's 
contribution to the development and support of an 
airport/airspace/airline simulation and decision support system. The 
School of Computer Science at the University of Nottingham have 
developed several airport optimisation and scheduling systems, some of 
which are running live at airports in various stages of their 
lifecycles, as well as having worked on both airspace and airline 
modelling and optimisation problems. These roles will enhance this 
portfolio by contributing to the creation of a combined 
airspace/airport/airline simulation and optimiser for use in evaluating 
the effects of potential changes to aircraft, airport, and airspace usage.


Applications are invited from highly skilled researchers in Computer 
Science, Mathematics, Optimisation, Operational Research, or closely 
related disciplines. The applicants must have or be close to completing 
a PhD in one of these disciplines. The successful candidates will have a 
good understanding of mathematical models for real world problems, of 
decision making/optimisation algorithms/approaches and of 
object-oriented software development.


The successful candidate should be able to work both independently and 
collaboratively and be able to meet tight deadlines. Good communication 
skills to liaise with airport, airline and aircraft partners are 
crucial. The candidate will be expected to disseminate appropriate 
research results in peer-reviewed journals and conferences.


The successful applicants will join the Computational Optimisation and 
Learning Lab at the University of Nottingham, School of Computer 
Science. They will work with Dr Jason Atkin, Dr Geert De Maere and Dr 
Daniel Karapetyan to develop a system to support and evaluate decisions 
related to a move towards using a fleet of electric aircraft. The role 
holders will spend most of their time on:

    1. Developing an integrated model of airport/airspace/airline 
operations;

    2. Designing an optimisation method for the integrated model;

    3. Performing the computational analysis, drawing conclusions, and 
reporting the findings.

These posts are available immediately and will be initially offered on a 
fixed-term contract until 31 May 2022. Extensions beyond this point may 
be possible subject to funding being available for the next phase of 
development. Hours of work are full-time (36.25 hours per week), however 
job share arrangements may be considered.


Informal enquiries may be addressed to Dr Jason Atkin, Dr Geert De Maere 
or Dr Daniel Karapetyan (Jason.Atkin at nottingham.ac.uk,
Geert.DeMaere at nottingham.ac.uk, Daniel.Karapetyan at nottingham.ac.uk). 
Please note that applications sent directly to this email address will 
not be accepted.

Applications should include both a CV and *a covering letter detailing 
how your knowledge/experience/skills meet each of the essential and any 
relevant desirable criteria in the Person Specification section of the 
Role Profile*.

    -

    Job Description/Role Profile
 
<https://jobs.nottingham.ac.uk/Upload/vacancies/files/32647/SCI045721.pdf>
    -

    Additional Information
    <https://jobs.nottingham.ac.uk/Org/AdditionalInfo.aspx?ref=SCI045721>
    -

    Apply Online
    <https://jobs.nottingham.ac.uk/org/app/start.aspx?jobId=32647>

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