Industrial PhD: Machine learning in food production: Applying computer vision in an automated management service for optimal climate control in pig ho

Aarhus Universitet (AU)

Ph.d. & forskning
Midtjylland
Østjylland (Aarhus)

Applications are invited for an Industrial PhD fellowship/scholarship at Graduate School of Science and Technology, Aarhus University, Denmark, within the Animal Science programme. The position is available from 1 November 2019.

Title:
Industrial PhD: Machine learning in food production: Applying computer vision in an automated management service for optimal climate control in pig houses

Research area and project description:
Scio+ and the Department of Animal Science (ANIS), Science and Technology, Aarhus University, invites applications for a 3-year Industrial PhD-scholarship in applying computer vision to monitor and improve animal production and welfare.

The overall objective of the current Industrial PhD project is to develop a management service for pig producers in order to identify periods of insufficient climate in pig pens. The PhD project will focus on three research areas:

    • Development of an automated real-time monitoring system that detects pigs’ lying pattern using computer vision techniques
    • Development of an early warning algorithm for detecting deviating lying patterns of the pigs, and
    • Implement climate intervention strategies to avoid the problems caused by inadequate thermal environment.
    The service is based on behavioural feedback reflecting the thermal need of pigs at pen level. An insufficient indoor climate in pig houses leads to thermal stress associated with negative effects on production and environment as well as health and welfare problems. An early sign of a sub-optimal thermal environment is a changed lying pattern of the pigs as an attempt to thermoregulate. Therefore, early detection of the lying pattern may be used to alert the farm manager and as a response adjust the climate control system accordingly.

    For more information about doing an industrial PhD research project, see both the website of Aarhus University and of Innovation Fund Denmark.

    Project responsibilities:
    The main activity for the Industrial PhD candidate will be:

    • Using camera technology and image analysis (computer vision) combined with data science techniques to correlate livestock behavior data and climate sensor data.
    • Develop and validate algorithms e.g. for detecting pigs’ lying pattern.
    • Present a solution for an automated management service that controls the climate in pig houses based on animal behavior and sensor inputs.

    The research will be done in collaboration with Data Scientists at Scio+ and researchers at Dept. of Animal Science, Aarhus University as well as with external partners.

    About Scio+
    Scio+ is a data science-driven company offering solutions to the food production sector (broiler, egg, and pig) around the globe. We offer farm production insights by applying machine learning techniques to production data and focus our work on what adds value to our customers. Insights are provided to customers via easy-to-use interfaces with attention to accuracy and focus on data security. We are located in an open office environment at Agro Food Park as part of the innovative food company cluster in Aarhus N, Denmark https://agrofoodpark.com/. We are an international group of skilled and highly motivated data scientists, software developers and domain experts. A large portion of the employees holds a Ph.D. and the majority a master’s degree within a natural science field ranging from computer science to field biology.

    http://www.scioplus.com

    LinkedIn page

    Qualifications and specific competences:
    As an Industrial PhD applicant, you must have:

    • A Master’s degree in computer science/engineering/data science or related fields, possibly with a biological or industrial background.
    • Experience with data analysis and coding in e.g. Python or R.
    • At least attained a grade of 10 (Danish scale) for any final thesis/examination project.
    • Meet one of the two levels for grade point averages:
      • For the Master’s and Bachelor’s degrees combined: A weighted average of at least 8.2 on the Danish 7-step scale or 9 on the Danish 13 scale
      • For a Danish two-year Master’s degree alone: A weighted average of at least 9.5 on the Danish 7-step scale or 9.4 on the Danish 13 scale

    Follow the link below to read more about qualifications and grade requirements for Danish and non-Danish educations:

    https://innovationsfonden.dk/sites/default/files/2019-03/guidelines-for-industrial-phd-06-07-2018.pdf

    Place of employment and place of work:
    Since it is an Industrial PhD project, the Industrial PhD candidate will be employed at Scio+ and enrolled at Graduate School of Science and Technology/GSST.

    The Industrial PhD candidate is expected to split the time equally between the company (Scio+, Agro Food Park 15, DK-8200 Aarhus N, Denmark) and the university (Research Centre Foulum, Blichers Allé 20, DK-8830 Tjele, Denmark), i.e. work equally in both places – the actual scheduling will be decided within the framework of the project.

    Contacts:
    Applicants seeking further information are invited to contact:

    Scio+, Data Science Manager René Thomsen, rene.thomsen@scioplus.com, +45 2857 8316.

    Professor Lene Juul Pedersen, Department of Animal Science, Head of the research section Animal Behaviour and Stress Biology, lene.juulpedersen@anis.au.dk / +45 8715 7907/ +45 5116 2822.

    Application procedures

    Before you applyInformation and attachments:
    Please be aware that you must have all relevant appendices, attachments, addresses for referees, etc. ready when you apply, as the entire application must be uploaded to the system in one go.

    Documentation of language skills:
    The English language requirement at GSST is comparable to an “English B level” in the Danish upper secondary school (“gymnasium”).

    English language qualifications comparable to an “English B level” are documented by one of the following tests:

    • TOEFL test, minimum score: 560 (paper-based test) or 83 (internet-based test). The paper-based test must have a “total score”. From the August 2019 call, GSST will no longer accept the paper-based test.
      Aarhus University does not accept the TOEFL ITP test. Aarhus University’s TOEFL code is 8935. Ask the test center to send your test results to Aarhus University, in order to enable verification of your test results.
    • IELTS (academic) test, minimum average score: 6.5 points
    • Cambridge English Language Assessment:
      Cambridge Certificate of Proficiency (CPE)
      Cambridge English: Certificate of Advanced English with grade A, B or C (CAE)
      Cambridge English: First Certificate with grade A (FCE)

    When to take the test and how to upload the documentation:
    The test result must not be more than two years old at the time of application.

    The English language test should be taken before applying for admission and uploaded under “language skills documentation” in the online application form.

    It is possible to apply for admission before you have taken the test. In this case, documentation stating that you have signed up for a test (please state expected submission date) must be uploaded. If the test result is not part of the original application the test result is to be sent to sphd@psys.au.dk no later than one month after the application deadline.

    The following applicants are exempted from documenting their English qualifications/taking a test:

    • Applicants with citizenship from the following countries: Australia, Canada, Ireland, New Zealand, the United Kingdom, the United States, or one of the Nordic countries (Denmark, Finland, Iceland, Norway or Sweden).
    • Applicants with a Bachelor’s or Master’s program completed in Australia, Canada, Ireland, New Zealand, the United Kingdom, or the United States. In this case, please upload your Bachelor’s or Master’s diploma under the section ”Language skills documentation”.
    • Applicants with a Bachelor’s or Master’s program completed at Aarhus University for which the requirement was English B level at the time of admission. In this case, please upload your Bachelor’s or Master’s diploma under the section” Language skills documentation”.

    The program committee may request further information or invite the applicant to attend an interview.

    How to apply:
    1) Find the application form:
    Go to www.phd.scitech.au.dk/for-applicants/apply-here - Note, the online application system opens on 1 June 2019.
    Choose August 2019 Call with deadline 1 August 2019 at noon (11.59 AM MET).
    You will be directed to the call and must choose the programme 'Animal Science'

    2) Fill in the following information:

    • Personal information
    • Academic background
    • Admission
    • Financing (if any)
    • Study: In the dropdown menu you must choose the project: "Machine learning in food production: Applying computer vision in an automated management service for optimal climate control in pig houses”
    • Source (how you found out about the call)

    Next to some of the information fields, you will find a number. Click on the number to get further directions on how to fill in the information field/what information is needed.

    3) Application attachments:
    Please be aware that you cannot submit the application if one or several of these documents have not been uploaded.

    If you wish to upload more than one document under each section, you must scan/merge all documents into one large PDF file and upload this. Please note that we reserve the right to remove scientific papers, large reports, theses and the like. Instead, you can indicate a URL where the information is available.

    Please note that all information in the application must be in Danish or English.

    As a minimum all applications must include (pdf-files only, max. 20 MB, no zip):

    • One reference (template for references)
    • Curriculum vitae,
    • Motivation (max. 1 page)
    • Transcripts, grade point averages (weighted and unweighted), and diploma(s) for both Bachelor’s and Master’s degree
    • Project description (½-4 pages). For technical reasons, you must upload a project description. When - as here - you apply for a specific project, please simply copy the project description above, and upload it as a PDF in the application. If you wish to, you can indicate an URL where further information can be found. Please note that we reserve the right to remove scientific papers, large reports, theses and the like.
    • Documentation of language skills if required.

    After submission of the application, you will receive a confirmation e-mail with an application ID, you should use for reference if needed. The e-mail will also include a link to the application – GSST urges you to check that all mandatory data, marked with an asterisk (*), is registered correctly and all attached files are readable. If there are any significant errors, you should reply to the confirmation e-mail with the correct details before the application deadline.

    GSST reserves the right to verify the authenticity of your educational diploma and transcripts:

    • Request additional information to verify an application.
    • Reject the application if it is proven, or if the University has a reasonable belief, that the information provided is false or if the applicant refuses to provide the requested information, whether or not an offer has already been made.
    • For further information on applying, assessment procedures, etc. please see the GSST Application Guide.

    Please note:

    • The program committee may request further information or invite the applicant to attend an interview.
    • The project will only be initiated if final funding (from GSST/the faculty) is secured.

    All interested candidates are encouraged to apply, regardless of their personal background.

    Contacts
    Lene Juul Pedersen
    Section Manager Professor M lene.juulpedersen@anis.au.dk
    H K27, 3208
    P +4587157907
    P +4551162822


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